A method for identifying lacustrine associated oil shale, mudstone and coal
Through sound wave time difference, gamma, density and resistivity logging technology, the logging curve range diagram that integrates these parameters solves the problem of difficult identification of oil shale, mudstone and coal distribution, achieving rapid and accurate identification and distinction, and reducing costs.
Patent Information
- Application Number
- CN202211404927.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-11-10
- Publication Date
- 2025-06-13
- Estimated Expiration
- 2042-11-10
AI Technical Summary
The prior art is difficult to systematically identify and distinguish the distribution of oil shale, mudstone and coal in the absence of outcrop and drilling centering, and the cost is high.
Sound wave time difference, gamma, density and resistivity logging technology are used to identify and distinguish the distribution of oil shale, mudstone and coal by fusing the logging curve range diagram of these parameters.
It realizes rapid and accurate identification of oil shale, mudstone and coal, reduces human identification errors and heart-taking costs, and is suitable for different regions.
Smart Images
Figure CN115680647B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of reservoir engineering, and particularly to a method for identifying lacustrine-associated oil shale, mudstone and coal. Background Art
[0002] Most of the oil shale in China is mainly produced in lacustrine environments. Usually, oil shale occurs as the top and bottom plates and is associated with coal. The oil shale is generally thin, but there are many layers, and the coal seams and mudstones are relatively thick. At present, the identification of oil shale, mudstone and their associated coal is mainly based on the differences in oil content and ash content, and systematic sampling and testing of oil shale, mudstone and coal are required. This not only has a high cost, but also it is difficult to systematically identify the distribution of oil shale, mudstone and coal in the absence of outcrops and drilling cores. Summary of the Invention
[0003] In view of the deficiencies of the prior art, the present invention provides a method for identifying lacustrine-associated oil shale, mudstone and coal. The method of the present invention for identifying lacustrine-associated oil shale, mudstone and coal based on acoustic travel time, gamma ray, density and resistivity logging. This method can not only quickly receive the parameter information of the logging instrument at different depths, but also quickly identify the thickness and distribution of oil shale and coal, greatly reducing the artificial identification error of cuttings logging and the cost of coring.
[0004] The technical solution of the present invention is: a method for identifying lacustrine-associated oil shale, mudstone and coal, characterized by comprising the following steps: a step of establishing standard identification data in the lacustrine area to be identified; a step of fusing the accurately identified oil shale, mudstone and coal data with the measured natural gamma ray, resistivity, density and acoustic travel time to form a logging curve range map of natural gamma ray, resistivity, density and acoustic travel time and oil shale, mudstone and coal; using the logging curve range map to identify the lithology of unknown drillings by measuring and analyzing two or more of the parameters of natural gamma ray, resistivity, density or acoustic travel time.
[0005] The beneficial effects of the present invention are: the present invention uses easily obtainable conventional logging data and has two advantages: one is that it can obtain logging signals in real time for identifying oil shale, mudstone and associated coal, and has a more accurate depth and a more systematic and clear vertical distribution characteristics of oil shale and coal; the other is that the identification method has the characteristics of universality and low cost, and this method is applicable to different regions. Brief Description of the Drawings
[0006] Figure 1 It is a logging curve range map. Detailed Description of the Invention
[0007] The natural gamma API, known as the standard calibration well, contains three different homogeneous radioactive formations in the well. The upper part is a low-radioactivity formation used to shield cosmic rays, the middle part is a high-radioactivity formation, and the bottom is a low-radioactivity layer. In the simulated formations of the high- and low-radioactivity formations, the instrument measures different counting rates, and 1 / 200 of the counting difference is defined as one API unit.
[0008] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.
[0009] A method for identifying lacustrine associated oil shale, mudstone and coal of the present invention includes the following steps:
[0010] First, establish standard identification data in the lacustrine area to be identified, that is, first accurately identify oil shale, mudstone and coal through conventional means such as distinguishing according to the differences in oil content and ash content at different depths by test wells; measure the natural gamma, resistivity, density and acoustic travel time information of the test wells at different depths.
[0011] Then, fuse the accurately identified oil shale, mudstone and coal data with the measured natural gamma, resistivity, density and acoustic travel time to form a log curve range map of natural gamma, resistivity, density and acoustic travel time and oil shale, mudstone and coal, as Figure 1 shown.
[0012] Finally, use the log curve range map to identify the lithology of unknown drillings by measuring and analyzing two or more parameters of natural gamma, resistivity, density or acoustic travel time.
[0013] As Figure 1 shown, the oil shale in the Tanshan area of our country is different from other lithologies such as shale and coal seams in the long-spacing gamma, resistivity and density of the log curve. Oil shale is rich in organic matter, and there is a certain positive correlation between the organic matter content and the content of radioactive elements (such as uranium and potassium) in the formation. Therefore, it shows a higher anomaly on the long-spacing gamma log curve. The presence of kerogen in oil shale is the main reason for its low resistivity, and mudstone usually has a higher resistivity, so mudstone and oil shale can be distinguished accordingly. Generally, the acoustic travel time of organic matter is greater than that of the rock skeleton. When the formation contains organic matter, the acoustic travel time of the formation will increase with the increase of the organic matter content. Therefore, the formation rich in organic matter has a larger acoustic travel time, that is, the acoustic travel time of oil shale is higher than that of mudstone. In terms of density, since the density of organic matter is small, when organic matter replaces the rock skeleton, the density of the rock will be reduced. Therefore, the density of the formation rich in organic matter is lower than that of normal mudstone.
[0014] As Figure 1As shown in the figure, the oil shale in the Tanshan area is associated with coal seams, or the oil shale alternates with coal seams and mudstones. It can be clearly seen from the characteristics of borehole logging curves that in the long-spacing gamma and acoustic travel-time logging, coal seam > oil shale > mudstone. Especially in the long-spacing gamma logging, the relationship is more obvious, and the curve shows a stepped or jump-like change characteristic. In the resistivity logging, the oil shale is significantly lower than the coal seam and the mudstone (coal seam > oil shale, mudstone > oil shale). In the density logging, mudstone > oil shale > coal seam, also showing a stepped change characteristic.
[0015] Due to the above characteristics, in the process of measuring and analyzing the steps of identifying unknown drilling lithology in the present invention, the method of predicting parameter weights can be adopted for prediction, that is, first establish the numerical ranges of natural gamma, resistivity, density, acoustic travel-time, etc. and corresponding prediction types and weights, as shown in Table 1. The thickness and distribution of oil shale and coal in other areas of the lake swamp can be predicted through the four parameters collected from other areas of the lake swamp.
[0016] Table 1 Prediction Parameter Weight Table
[0017]
[0018] For example, when collecting data at 120 meters in other areas, the natural gamma is 1800 API, the resistivity is 280 Ω·m, the density is 1.8 g / cm 3 , and the acoustic travel-time is 180 μs / m. Then, according to the corresponding table, the weight of the coal seam can be calculated to be 10. Therefore, when predicting, it is very likely that the layer at 120 meters is a coal seam. And when the present invention is predicting, there will be a certain section of data that is very high and points to a certain layer. For example, when the total weight of the coal seam from 100 meters to 120 meters is 11, and the total weight of the oil shale from 125 meters to 135 meters is 13. At the junction, the weights of both the coal seam and the oil shale are relatively the highest values and decrease. This can indicate that the junction between the coal seam and the oil shale is at 120 meters and 125 meters. By judging the peak value and the junction value, the change trends of the oil shale, mudstone, and coal can be clarified, and the storage volume can be predicted more accurately. The prediction accuracy rate of the present invention at the peak is higher than 95%, and the prediction accuracy rate at the junction is also higher than 60%.
[0019] As another way of the present invention, when predicting weights, the method of predicting weights with two parameters of natural gamma and density can be adopted for prediction, or the method of predicting weights with three parameters of natural gamma, density plus resistivity, or natural gamma, density plus acoustic travel-time can also be adopted for prediction.
Claims
1. A method for identifying lacustrine associated oil shale, mudstone and coal, characterized in that: It includes the following steps: The step of establishing standard identification data in the lacustrine area to be identified; The step of fusing the accurately identified oil shale, mudstone and coal data with the measured natural gamma, resistivity, density and acoustic travel time to form a log curve range map of natural gamma, resistivity, density, acoustic travel time and oil shale, mudstone and coal; Using the log curve range map to identify the lithology of unknown drill wells by measuring and analyzing more than two parameters of natural gamma, resistivity, density or acoustic travel time; adopting the method of parameter weight prediction for prediction, first corresponding the parameter value range with the prediction type and weight, and identifying by calculating the weight through the measurement of the parameter; The table corresponding the parameter value range with the prediction type and weight is: Through the determination of peak value and boundary value, the change trend of oil shale, mudstone and coal can be clarified, and the storage can be predicted more accurately.
2. The method for identifying lacustrine associated oil shale, mudstone and coal according to claim 1, characterized in that: The process of the step of establishing standard identification data is: accurately identifying oil shale, mudstone and coal according to the differences in oil content and ash content at different depths through test wells; measuring the natural gamma, resistivity, density and acoustic travel time information of the test wells at different depths.
3. The method for identifying lacustrine associated oil shale, mudstone and coal according to claim 1, characterized in that: Adopt the way of weight prediction with two parameters of natural gamma and density for prediction.