Reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics
By performing specific processing on the core training set to obtain resistivity data, a reservoir permeability parameter prediction model is constructed based on the characteristics of differential depth and shallow resistivity, which solves the problem that the permeability parameters of low-porous permeability reservoirs are difficult to accurately predict, and achieves higher accuracy and lower cost permeability parameter prediction.
Patent Information
- Application Number
- CN202411829831.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-12
- Publication Date
- 2025-03-18
AI Technical Summary
The prior art is difficult to accurately evaluate the permeability parameters of low-porous permeability reservoirs, resulting in misjudgment of oil and gas reservoir production capacity and affecting oil and gas reservoir development and production work.
By collecting the core training set, saturated formation water treatment and displacement treatment are carried out to obtain the core resistivity of the pure oil layer, and drilling fluid intrusion is performed on the core training set of the pure oil layer to obtain the core resistivity of the drilling fluid intrusion. The initial reservoir permeability parameter prediction model is constructed based on the characteristics of different depth and shallow resistivity, and the least squares method is fitted to obtain the final reservoir permeability parameter prediction model.
The prediction accuracy of the permeability of low-porous permeability reservoirs is improved, and the permeability parameters of a single depth point in the reservoir section can be reflected, cost is reduced, and it is suitable for the evaluation and development of low-porous permeability oil and gas reservoirs.
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Figure CN119646521A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of oil and natural gas exploration, and in particular to a reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics. Background Art
[0002] The pore structure of low-porosity and permeability reservoirs is complex, and the micropores are well developed and compaction and cementation are serious, which makes the relationship between reservoir porosity and permeability complex. Under the same porosity conditions, the permeability parameters often show orders of magnitude differences, making it difficult for geologists to accurately evaluate the permeability parameters of low-porosity and permeability reservoirs. However, the permeability parameter is a key parameter that determines the productivity of oil and gas layers. If used improperly, it will lead to misjudgment of the productivity of oil and gas reservoirs and affect the development and production of oil and gas reservoirs.
[0003] The existing method obtains the reservoir production pressure through drill pipe formation testing, and uses the well test analysis process to perform time fitting on the formation production pressure to obtain the formation permeability. The permeability parameters obtained by the existing method have the highest accuracy, but the cost is high. At the same time, the permeability obtained by this method is the average response value of the layer segment, which cannot reflect the permeability parameters of a single depth point in the reservoir segment. Summary of the invention
[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics.
[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:
[0006] A reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics comprises the following steps:
[0007] S1, collecting a core training set of a low porosity and permeability reservoir, and obtaining the porosity and permeability parameters of the core training set;
[0008] S2. Performing saturated formation water treatment and displacement treatment on the core training set to obtain the pure oil layer core resistivity of the core training set, and performing drilling fluid invasion treatment on the pure oil layer core training set to obtain the drilling fluid invasion core resistivity of the core training set;
[0009] S3. Based on the deep and shallow resistivity difference characteristics, an initial reservoir permeability parameter prediction model is constructed. Based on the porosity, permeability parameters, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core training set, the initial reservoir permeability parameter prediction model is fitted using the least squares method to obtain the final reservoir permeability parameter prediction model.
[0010] S4. Collect the core to be predicted, obtain the porosity, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core to be predicted, and use the final reservoir permeability parameter prediction model to obtain the reservoir permeability parameter prediction result.
[0011] Furthermore, in step S1, a core training set of low porosity and permeability reservoirs is collected. The specific process is: cores of low porosity and permeability reservoirs with different physical properties are drilled using core sampling equipment, and the cores of low porosity and permeability reservoirs with different physical properties are washed with oil, dried and vacuumed to collect a core training set of low porosity and permeability reservoirs.
[0012] Further, in step S2, the core training set is subjected to saturated formation water treatment and displacement treatment to obtain the pure oil layer core resistivity of the core training set. The specific process is: the core training set is subjected to saturated formation water treatment to obtain a core training set with a water saturation of 100% in the core, and then a displacement device whose displacement fluid is crude oil is used to increase the pressure of the core training set with a water saturation of 100% in the core to displace the core, obtain the core training set in which the core reaches the bound water state and determines it as the pure oil layer core of the core training set, and measure its resistivity to obtain the pure oil layer core resistivity of the core training set.
[0013] Further, in step S2, the core training set of the pure oil layer is subjected to drilling fluid invasion processing to obtain the drilling fluid invasion core resistivity of the core training set. The specific process is: the core training set of the pure oil layer is subjected to drilling fluid invasion using a drilling fluid invasion device with set salinity, and a pressure pump is used to control the core displacement pressure to displace the crude oil in the core, the drilling fluid invasion parameters of the core are determined, and its resistivity is measured to obtain the drilling fluid invasion core resistivity of the core training set.
[0014] Furthermore, the mineralization is set to 80000 PPm.
[0015] Furthermore, the drilling fluid invasion parameters of the core include drilling fluid invasion pressure difference and drilling fluid invasion time.
[0016] Furthermore, in step S3, an initial reservoir permeability parameter prediction model is constructed based on the deep and shallow resistivity difference characteristics, which is expressed as:
[0017] Perm=a*exp(b*Por+c*P)
[0018] Where: Perm is the predicted value of reservoir permeability parameter, a is the first coefficient, exp is the exponential function, b is the second coefficient, Por is the porosity of the core, c is the third coefficient, and P is the ratio of the pure oil layer core resistivity of the core to the resistivity of the drilling fluid invading the core.
[0019] Further, the first coefficient is determined to be 0.3638, the second coefficient is determined to be 0.1847, and the third coefficient is determined to be 0.44.
[0020] The beneficial effects of the present invention are:
[0021] (1) The present invention performs saturated formation water treatment and displacement treatment on the core training set to obtain the pure oil layer core resistivity of the core training set, and performs drilling fluid invasion treatment on the pure oil layer core training set to obtain the drilling fluid invasion core resistivity of the core training set, and then constructs an initial reservoir permeability parameter prediction model based on the deep and shallow resistivity difference characteristics, and uses the least squares method to fit the initial reservoir permeability parameter prediction model to obtain the final reservoir permeability parameter prediction model, which can improve the prediction accuracy of the permeability of low porosity and permeability reservoirs, and the method is easy to operate, can reflect the permeability parameters of a single depth point in the reservoir section, and is more suitable for the application of low porosity and permeability oil and gas reservoir reserve evaluation and development work;
[0022] (2) The present invention does not require the collection of additional geological basic data and does not incur data collection operation fees. The permeability parameters can be predicted by simply processing and mining the existing geological data, which has lower costs than existing methods. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 The figure is a flow chart of a reservoir permeability parameter prediction method based on the deep-shallow resistivity difference characteristics. DETAILED DESCRIPTION
[0024] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.
[0025] like Figure 1 As shown, a reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics includes the following steps:
[0026] S1. Collect a core training set of low porosity and permeability reservoirs, and obtain the porosity and permeability parameters of the core training set.
[0027] In an optional embodiment of the present invention, the present invention collects a core training set of low porosity and permeability reservoirs, and the specific process is: using a core sampling device to drill cores of low porosity and permeability reservoirs with different physical properties, and washing, drying and vacuuming the cores of low porosity and permeability reservoirs with different physical properties to collect a core training set of low porosity and permeability reservoirs.
[0028] Specifically, the present invention uses a core sampling device to drill 20 cores of low porosity and permeability reservoirs with different physical properties. After collecting a core training set of low porosity and permeability reservoirs, the present invention uses a porosity and permeability analyzer to measure the porosity and permeability parameters of the 20 cores in the core training set, and obtains the porosity and permeability parameters of the 20 cores, so as to obtain the porosity of the core training set and the permeability parameters of the core training set.
[0029] S2. Performing saturated formation water treatment and displacement treatment on the core training set to obtain the pure oil layer core resistivity of the core training set, and performing drilling fluid invasion treatment on the pure oil layer core training set to obtain the drilling fluid invasion core resistivity of the core training set.
[0030] In an optional embodiment of the present invention, the present invention performs saturated formation water treatment and displacement treatment on the core training set to obtain the pure oil layer core resistivity of the core training set. The specific process is: perform saturated formation water treatment on the core training set to obtain a core training set with a water saturation of 100% in the core, and then use a displacement device whose displacement fluid is crude oil to increase the pressure of the core training set with a water saturation of 100% in the core to displace it, obtain the core training set in which the core reaches a bound water state and determines it as a pure oil layer core of the core training set, and measure its resistivity to obtain the pure oil layer core resistivity of the core training set.
[0031] Specifically, the core training set is saturated with formation water in the present invention, and the core is placed in a vacuum environment and saturated with water for 24 hours. The present invention uses a resistivity meter to measure the pure oil layer core of the core training set to obtain the pure oil layer core resistivity of the core training set.
[0032] The present invention performs drilling fluid invasion processing on a core training set of pure oil layers to obtain the drilling fluid invasion core resistivity of the core training set. The specific process is: using a drilling fluid invasion device with set salinity to perform drilling fluid invasion on the core training set of pure oil layers, and using a pressure pump to control the core displacement pressure to displace the crude oil in the core, determining the drilling fluid invasion parameters of the core, and measuring its resistivity to obtain the drilling fluid invasion core resistivity of the core training set.
[0033] In the present invention, the mineralization is set to 80000 PPm, which can make the core truly represent the state of reservoir drilling fluid invasion in the actual wellbore environment, thereby improving the prediction accuracy of the permeability of low-porosity and permeability reservoirs.
[0034] The drilling fluid invasion parameters of the core in the present invention include the drilling fluid invasion pressure difference and the drilling fluid invasion time. The present invention takes into account that the same core will have different drilling fluid invasion core resistivity under different invasion parameter conditions. Therefore, the present invention can reduce the error of the drilling fluid invasion core resistivity of the core training set by determining the drilling fluid invasion pressure difference and the drilling fluid invasion time, and then using a resistivity meter to measure and determine the resistivity of the core after the drilling fluid invasion under the drilling fluid invasion pressure difference and the drilling fluid invasion time.
[0035] S3. An initial reservoir permeability parameter prediction model is constructed based on the deep and shallow resistivity difference characteristics. Based on the porosity, permeability parameters, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core training set, the initial reservoir permeability parameter prediction model is fitted using the least squares method to obtain the final reservoir permeability parameter prediction model.
[0036] In an optional embodiment of the present invention, the present invention constructs an initial reservoir permeability parameter prediction model based on the deep and shallow resistivity difference characteristics, which is expressed as:
[0037] Perm=a*exp(b*Por+c*P)
[0038] Where: Perm is the predicted value of reservoir permeability parameter, a is the first coefficient, exp is the exponential function, b is the second coefficient, Por is the porosity of the core, c is the third coefficient, and P is the ratio of the pure oil layer core resistivity of the core to the resistivity of the drilling fluid invading the core.
[0039] In the present invention, the porosity, permeability parameters, pure oil layer core resistivity and drilling fluid invasion core resistivity of 20 cores in the core training set are obtained, and the ratio between the pure oil layer core resistivity and the drilling fluid invasion core resistivity is calculated to obtain the deep and shallow resistivity ratio, as shown in the following table:
[0040]
[0041] In the above table, the core No. 5 and No. 15 have a porosity of 17.1% and a permeability of 28.4 mD; while the porosity of the core No. 15 is 17.1% and the permeability is 12.4 mD; under the same drilling fluid invasion time and invasion pressure difference, the deep-shallow resistivity ratio of the core No. 5 is 1.68, while the deep-shallow resistivity ratio of the core No. 15 is 1.30. In actual logging, since the shallow resistivity detection depth is shallow, the shallow resistivity measurement value is the rock resistivity of the flushing zone after the drilling fluid invasion. Therefore, in the actual wellbore logging environment, the shallow resistivity is equivalent to the core resistivity after the drilling fluid invasion in the above table, while the deep resistivity detection depth is deep, and the original formation resistivity can be detected, which is equivalent to the pure oil layer core resistivity in the above table. It can be seen that the core permeability parameter is related to the difference characteristics of the deep and shallow resistivity. Then, according to the rock physical data, the present invention determines that the reservoir permeability has a general relationship with the porosity. Therefore, the present invention constructs the above-mentioned initial reservoir permeability parameter prediction model based on the deep and shallow resistivity difference characteristics.
[0042] Based on the data in the above table, the present invention uses the least squares method to fit the initial reservoir permeability parameter prediction model to obtain the final reservoir permeability parameter prediction model, wherein the first coefficient is determined to be 0.3638, the second coefficient is determined to be 0.1847, and the third coefficient is determined to be 0.44.
[0043] S4. Collect the core to be predicted, obtain the porosity, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core to be predicted, and use the final reservoir permeability parameter prediction model to obtain the reservoir permeability parameter prediction result.
[0044] In an optional embodiment of the present invention, the present invention collects the core to be predicted, obtains the porosity, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core to be predicted in the above manner, and substitutes the porosity, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core to be predicted into the final reservoir permeability parameter prediction model to obtain the reservoir permeability parameter prediction result.
[0045] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.
Claims
1. A reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics, characterized in that: The following steps are involved: S1, collecting a core training set of a low porosity and permeability reservoir, and obtaining the porosity and permeability parameters of the core training set; S2. Performing saturated formation water treatment and displacement treatment on the core training set to obtain the pure oil layer core resistivity of the core training set, and performing drilling fluid invasion treatment on the pure oil layer core training set to obtain the drilling fluid invasion core resistivity of the core training set; S3. Based on the deep and shallow resistivity difference characteristics, an initial reservoir permeability parameter prediction model is constructed. Based on the porosity, permeability parameters, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core training set, the initial reservoir permeability parameter prediction model is fitted using the least squares method to obtain the final reservoir permeability parameter prediction model. S4. Collect the core to be predicted, obtain the porosity, pure oil layer core resistivity and drilling fluid invasion core resistivity of the core to be predicted, and use the final reservoir permeability parameter prediction model to obtain the reservoir permeability parameter prediction result.
2. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 1 is characterized in that: In step S1, a core training set of low porosity and permeability reservoirs is collected. The specific process is: cores of low porosity and permeability reservoirs with different physical properties are drilled using core sampling equipment, and the cores of low porosity and permeability reservoirs with different physical properties are washed with oil, dried and vacuumed to collect a core training set of low porosity and permeability reservoirs.
3. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 1 is characterized in that: In step S2, the core training set is subjected to saturated formation water treatment and displacement treatment to obtain the pure oil layer core resistivity of the core training set. The specific process is: the core training set is subjected to saturated formation water treatment to obtain a core training set with a water saturation of 100% in the core, and then a displacement device whose displacement fluid is crude oil is used to increase the pressure of the core training set with a water saturation of 100% in the core to displace the core, obtain the core training set in which the core reaches a bound water state and determines it as the pure oil layer core of the core training set, and measure its resistivity to obtain the pure oil layer core resistivity of the core training set.
4. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 1 is characterized in that: In step S2, drilling fluid invasion processing is performed on the core training set of the pure oil layer to obtain the drilling fluid invasion core resistivity of the core training set. The specific process is: drilling fluid invasion is performed on the core training set of the pure oil layer using a drilling fluid invasion device with set salinity, and a pressure pump is used to control the core displacement pressure to displace the crude oil in the core, the drilling fluid invasion parameters of the core are determined, and its resistivity is measured to obtain the drilling fluid invasion core resistivity of the core training set.
5. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 4 is characterized in that: The mineralization is set to 80000PPm.
6. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 4 is characterized in that: The drilling fluid invasion parameters of the core include the drilling fluid invasion pressure difference and the drilling fluid invasion time.
7. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 1 is characterized in that: In step S3, an initial reservoir permeability parameter prediction model is constructed based on the deep and shallow resistivity difference characteristics, which is expressed as: Perm=a*exp(b*Por+c*P) Where: Perm is the predicted value of reservoir permeability parameter, a is the first coefficient, exp is the exponential function, b is the second coefficient, Por is the porosity of the core, c is the third coefficient, and P is the ratio of the pure oil layer core resistivity of the core to the resistivity of the drilling fluid invading the core.
8. The reservoir permeability parameter prediction method based on deep and shallow resistivity difference characteristics according to claim 7 is characterized in that ,The first coefficient is determined to be 0.3638, the second coefficient is determined to be 0.1847, and the third coefficient is determined to be 0.44.