Target stratum paleo-water depth recovery method and device and storage medium
By determining the sedimentary period and astronomical cyclone orbit parameters in the target strata, combining the relationship between GR data and radioactive element content, a water depth noise correlation model was established, which solved the problem of difficulty in accurately regaining the pastoral depth in the existing technology, and achieved rapid and accurate quantitative reconstruction of the pastoral depth.
Patent Information
- Application Number
- CN202411760634.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-03
- Publication Date
- 2025-04-25
AI Technical Summary
The existing paleowater depth recovery methods are difficult to accurately quantify the paleowater depth distribution during the sedimentary period, and are mostly qualitative or semi-quantitative.
By selecting the target strata, the corresponding strata sedimentary period and astronomical cyclone orbit parameters are determined, the sedimentary noise background parameters are established, the water depth noise correlation model is established, and the paleowater depth of the target strata is quantitatively predicted.
The rapid and accurate quantitative reconstruction of the paleowater depth is achieved, which avoids the problem of re-drilling, and can obtain multi-point continuous paleowater depth data in batches, improving the efficiency of quantitative reconstruction of paleowater depth.
Smart Images
Figure CN119885555A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of ancient water depth recovery in geochemistry and geophysics, and in particular to a method, equipment and storage medium for recovering ancient water depth of a target stratum. Background Art
[0002] Ancient water depth is the main influencing factor of various biological, chemical and physical parameter characteristics in the marine environment. Ancient water depth restoration is of great significance for exploring ancient environmental changes. The dynamic sedimentary noise model to restore relative ancient water depth is a method that has just emerged in recent years to reflect the relative change of ancient water depth. The change of noise in the sediment is extracted by astronomical cycles, and a correlation is established between it and the sea level change. A good correlation is obtained, indicating that the noise in the sediment has a good relationship with the sea level. The main principle of restoring the relative water depth change under this model is to filter out the Milankovitch orbit parameter signal in the astronomical cycle, match the strength of the remaining noise signal with the sea level change, and finally establish the relationship between sedimentary noise and water depth change. The radioactive element method also provides a basis for the restoration of ancient water depth. Studies have shown that there is a high correlation between the U element content and the abundance of organic carbon (TOC). The enrichment of U (uranium) element is mainly due to the reduction and adsorption of U by organic matter during the diagenesis process. With the increase of water depth, the content of organic carbon also increases significantly, indicating that there is a positive correlation between water depth and organic carbon content. The U element curve can better reflect the trend of paleo-water depth changes and is positively correlated with the paleo-water depth. The existing methods for recovering paleo-water depth mainly include sedimentology, geochemistry, paleontology and well logging. Most of these methods are qualitative or semi-quantitative paleo-water depth variation ranges, and it is difficult to accurately quantify the distribution of paleo-water depth during the deposition period. Summary of the invention
[0003] In view of this, the present invention provides a method for recovering ancient water depth of a target stratum, comprising the following steps:
[0004] S1: Select the target stratum to obtain the key parameters for paleo-water depth restoration of the target stratum;
[0005] S2: Determine the stratigraphic deposition period corresponding to the target stratigraphic layer, and obtain the astronomical cyclotron orbit parameters corresponding to the target stratigraphic layer;
[0006] S3: Obtain sediment noise background parameters based on the key parameters of the paleo-water depth of the target stratum and its corresponding astronomical cyclic orbit parameters;
[0007] S4: Establish a water depth noise correlation model based on the sediment noise background parameters, establish the relationship between sediment noise and water depth change, qualitatively characterize the paleo-water depth change of the target stratum, and obtain the sedimentary environment of the target stratum;
[0008] S5: Establish a potential relationship model between the GR data in known wells and the content of radioactive elements;
[0009] S6: Bring the GR data of the target well into the potential relationship model in step S5, fit the element changes of the radioactive element content of the target formation, and quantitatively predict the paleo-water depth of the target formation of the target well based on the quantitative change relationship between the radioactive element content and the paleo-water depth of the sedimentary formation.
[0010] Furthermore, the process of obtaining the sediment noise background parameters in step S3 is: analyzing the key parameter data of paleo-water depth restoration to obtain the formation noise data, removing the astronomical cyclonic orbit parameters in the formation noise data, and obtaining the sediment noise background parameters.
[0011] Furthermore, the process of analyzing the key parameter data for paleo-water depth restoration in step S3 is: using the Acyle time series analysis package to analyze the key parameter data for paleo-water depth restoration to obtain the GR data of the target formation, and using the noise model to analyze the GR data to obtain the formation noise data.
[0012] Furthermore, the noise model is a Rho-1 noise model.
[0013] Furthermore, the corresponding radioactive element in step S5 is U element.
[0014] Furthermore, the process of selecting the target stratum in step S1 is: conducting a geological background survey; selecting a paleo-water depth study area; obtaining the target well location and sedimentary rock samples therein within the paleo-water depth study area; analyzing the characteristics of the sedimentary rock samples, and selecting the stratum where the sedimentary rock samples having the lithofacies sensitivity curve are located as the target stratum.
[0015] Furthermore, in step S5, a potential relationship model between the GR data in known wells and the radioactive element content is established: geological data of multiple known strata with the same sedimentary period and sedimentary environment as the target strata are obtained, and the correlation between the pure mudstone GR data and the corresponding radioactive element content in the geological data of the known strata is analyzed, so as to establish a potential relationship model between the GR data in the known wells and the radioactive element content.
[0016] Furthermore, the process of establishing the potential relationship model in step S5 is: using Excel to project the U element and the GR curve value into a plane coordinate, selecting the fitting method with the highest correlation coefficient to obtain the empirical formula between the two, and establishing the conversion relationship between GR data and U. The conversion relationship is the potential relationship model between the GR data and the content of the radioactive element.
[0017] The present invention also provides a target stratum paleo-water depth recovery storage medium and a target stratum paleo-water depth recovery device; the storage medium, storage instructions and data are used to implement the above-mentioned target stratum paleo-water depth recovery method.
[0018] The target formation paleo-water depth recovery device comprises a processor and the storage medium, and the processor loads and executes instructions and data in the storage medium to implement the target formation paleo-water depth recovery method.
[0019] The beneficial effect of a target formation ancient water depth recovery method of the present invention is as follows: the method selects known drilling and logging data, filters out residual sedimentary noise through astronomical cyclotron orbit related parameters, and then corrects it through parameter sensitivity, measurement error, etc. to obtain water depth related noise and other noise, establishes a sedimentary noise model and obtains the relationship with water depth changes, and then uses known target wells to establish the relationship between the U element and the GR curve, so as to quickly and quantitatively reconstruct the ancient water depth.
[0020] This method can directly use natural gamma curves or magnetic susceptibility curves to perform astronomical cycle and time series analysis, and can directly calculate the noise variation curve through ACYCLE software. It has the advantages of relatively convenient data selection and operation, and the results obtained are highly continuous.
[0021] This method can use existing data to establish a potential relationship model between GR data and radioactive element content, which not only avoids the need to re-drill, but also can obtain relatively accurate and continuous paleowater depth data at multiple points in batches, which can greatly improve the efficiency of quantitative reconstruction of paleowater depth of target strata, thereby effectively promoting sedimentary paleoenvironment research and the exploration and development of oil and gas resources. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 A flow chart of a method for recovering ancient water depth of a target stratum according to the present invention;
[0023] Figure 2 is the response relationship of the two noise models to noise in the embodiment of the present invention;
[0024] Figure 3 is the response of the U element to the paleo-water depth in the embodiment of the present invention;
[0025] Figure 4 This is a single well paleo-water depth recovery diagram in an embodiment of the present invention;
[0026] Figure 5 This is a quantitative paleo-water depth recovery diagram of connected wells in an embodiment of the present invention;
[0027] Figure 6 This is a restored map of the ancient water depth of the connected wells in an embodiment of the present invention;
[0028] Figure 7 It is a working schematic diagram of the hardware device of the present invention. DETAILED DESCRIPTION
[0029] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.
[0030] Please refer to Figure 1 , Figure 1 The present invention is a flow chart of a method for recovering the ancient water depth of a target stratum. The method for recovering the ancient water depth of a target stratum comprises the following steps:
[0031] S1: Select the target formation and obtain the key parameters for paleo-water depth recovery of the target formation; the key parameters for paleo-water depth recovery include natural gamma logging curve (GR) data, radioactive element (U) content, etc.
[0032] The process of selecting the target stratum is as follows: conduct a geological background survey, and determine the area to be explored as the paleo-water depth study area according to research needs; the paleo-water depth study area has multiple exploration wells that have been drilled, and the wells with rich geological data are selected as target wells; obtain the location of the target wells and their sedimentary rock samples in the study area; analyze the characteristics of the sedimentary rock samples, and select the stratum where the sedimentary rock samples with lithofacies sensitivity curves including the GR curve are located as the target stratum.
[0033] S2: Determine the stratigraphic deposition period corresponding to the target stratigraphic layer, and obtain the astronomical cyclotron orbit parameters corresponding to the target stratigraphic layer;
[0034] The stratigraphic deposition age corresponding to the target stratum can be obtained by consulting known data or according to the above-mentioned sedimentary rock samples and their geological parameters. The astronomical cycle orbit parameters include E, e, O, and P, which represent the long eccentricity, short eccentricity, slope, and precession, respectively. In this example, the target strata are mainly the Sanya Formation, Meishan Formation, and Lingshui Formation of the Neogene, so the required astronomical cycle orbit parameters are roughly corresponding to the boundary period between the Neogene and the Paleogene;
[0035] S3: Obtain sediment noise background parameters based on the key parameters of the paleo-water depth of the target stratum and its corresponding astronomical cyclic orbit parameters;
[0036] The process of obtaining the sediment noise background parameters in step S3 is: analyzing the key parameter data of paleo-water depth restoration to obtain the formation noise data, removing the astronomical cyclonic orbit parameters in the formation noise data, and obtaining the sediment noise background parameters.
[0037] The process of analyzing the key parameter data of paleo-water depth restoration in step S3 is: using the Acyle time series analysis package to analyze the key parameter data of paleo-water depth restoration to obtain the GR data of the target stratum, and using the noise model to analyze the GR data to obtain the stratum noise data. The noise model selected in this example is the Rho-1 noise model, which is a known noise model and is widely used in the fields of image processing, control, automation and computers.
[0038] Based on the Rho-1 noise model, the steps of correcting the deposition noise by using parameter sensitivity, measurement error, etc. specifically include correcting the sliding window size and sliding window movement rate required in the noise model analysis process, and also correcting the number of Monte Carlo simulations to ensure the measurement error in the noise analysis process, and finally correcting the noise analysis results (reference Figure 2 );
[0039] By means of sedimentary lithology characteristics, geological background investigation, data curve change characteristics and abnormal changes in the background value of the noise model, the relevant noise caused by tectonic activities and emergencies is eliminated. For example, in this example (reference Figure 3 ), a small amount of sandstone thin layers developed at the bottom of the Sanya Formation, and a small amount of sandy strata developed in the middle of the second section of the Sanya Formation. Therefore, based on the actual lithology and sedimentary background, it can be preliminarily determined that the sedimentary paleo-water depth at these locations is relatively shallow, the noise background is large, and there may be sandy deposits caused by bottom currents and other events. At the same time, these locations show low values and top and bottom mutation characteristics on the GR curve. It is also necessary to consider whether the noise results of these sections are consistent with the actual sedimentary conditions.
[0040] S4: Establish a water depth noise correlation model based on the sediment noise background parameters, establish the relationship between sediment noise and water depth change, qualitatively characterize the paleo-water depth change of the target stratum, and obtain the sedimentary environment change of the target stratum (reference Figure 3 ).
[0041] S5: Establish a potential relationship model between the GR data in the known well and the content of the radioactive element; in this example, the radioactive element content is U (uranium). The process of establishing the potential relationship model is: using Excel to project the U element and the GR curve value on a plane coordinate, select the fitting method with the highest correlation coefficient to obtain the empirical formula between the two, and establish the conversion relationship between GR and U. This conversion relationship is the potential relationship model between the GR data and the radioactive element U content (reference Figure 4 ).
[0042] S6: Bring the GR data of the target well into the potential relationship model in step S5, fit the element changes of the radioactive element content of the target formation, and quantitatively predict the paleo-water depth of the target formation of the target well based on the quantitative change relationship between the radioactive element content and the paleo-water depth of the sedimentary formation.
[0043] In a preferred embodiment, the process of quantitatively predicting the ancient water depth of the target formation of the target well in step S6 is: investigate or measure the maximum organic carbon content value of the basin where the target well is located, and establish the correlation between the U element and the ancient water depth based on this value; investigate or obtain the sedimentary environment of the basin where the target well is located based on relevant information such as lithology and geochemical elements, and judge the maximum ancient water depth that may develop under the sedimentary environment; calculate the maximum ancient water depth of the target single well based on the relationship between GR and the U element, and the relationship between the U element and the ancient water depth; perform correlation fitting based on the maximum ancient water depth of the target single well and the actual maximum ancient water depth of the sedimentary background, and quantitatively reconstruct the ancient water depth of the target formation (reference Figure 5 and Figure 6 ).
[0044] See also Figure 7 , Figure 7 It is a working diagram of the hardware device of an embodiment of the present invention, and the hardware device specifically includes: a target formation paleo-water depth recovery device, and the device includes a processor and a storage medium.
[0045] Processor: The processor loads and executes the instructions and data in the storage medium to achieve the above-mentioned target formation ancient water depth recovery.
[0046] Storage medium: The storage medium stores instructions and data for implementing the above-mentioned paleo-geomorphology quantitative characterization method.
[0047] In this document, the directional words such as front, back, top, and bottom are defined by the relative positions of the components in the drawings and between the components, and are only for the sake of clarity and convenience in expressing the technical solution. It should be understood that the use of the directional words should not limit the scope of protection requested by this application.
[0048] In the absence of conflict, the above embodiments and features in the embodiments may be combined with each other.
[0049] 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 principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for recovering ancient water depth of a target stratum, characterized in that: The steps include: S1: Select the target stratum and obtain the key parameters for paleo-water depth restoration of the target stratum; S2: Determine the stratigraphic deposition period corresponding to the target stratigraphic layer, and obtain the astronomical cyclotron orbit parameters corresponding to the target stratigraphic layer; S3: Obtain sediment noise background parameters based on the key parameters of the paleo-water depth of the target stratum and its corresponding astronomical cyclic orbit parameters; S4: Establish a water depth noise correlation model based on the sediment noise background parameters, establish the relationship between sediment noise and water depth change, qualitatively characterize the paleo-water depth change of the target stratum, and obtain the sedimentary environment of the target stratum; S5: Establish a potential relationship model between the GR data in known wells and the content of radioactive elements; S6: Bring the GR data of the target well into the potential relationship model in step S5, fit the element changes of the radioactive element content of the target formation, and quantitatively predict the paleowater depth of the target formation of the target well based on the changing relationship between the radioactive element content and the paleowater depth of the sedimentary formation.
2. A method for recovering ancient water depth of a target stratum according to claim 1, characterized in that: The process of obtaining the sediment noise background parameters in step S3 is: analyzing the key parameter data of paleo-water depth restoration to obtain the formation noise data, removing the astronomical cyclonic orbit parameters in the formation noise data, and obtaining the sediment noise background parameters.
3. A method for recovering ancient water depth of a target stratum according to claim 2, characterized in that: The process of analyzing the key parameter data for paleo-water depth restoration in step S3 is as follows: using the Acycle time series analysis package to analyze the key parameter data for paleo-water depth restoration to obtain the GR data of the target formation, and using the noise model to analyze the GR data to obtain the formation noise data.
4. A method for recovering ancient water depth of a target stratum according to claim 2, characterized in that: The noise model is a Rho-1 noise model.
5. A method for recovering ancient water depth of a target stratum according to claim 1, characterized in that: The corresponding radioactive element in step S5 is U element.
6. A method for recovering ancient water depth of a target stratum according to claim 1, characterized in that: The process of selecting the target stratum in step S1 is: conducting a geological background survey; selecting a paleo-water depth study area; obtaining the target well location and sedimentary rock samples in the paleo-water depth study area; analyzing the characteristics of the sedimentary rock samples, and selecting the stratum where the sedimentary rock samples with the lithofacies sensitivity curve are located as the target stratum.
7. A method for recovering ancient water depth of a target stratum according to claim 1, characterized in that: In step S5, a potential relationship model between the GR data in known wells and the radioactive element content is established: the geological data of multiple known strata with the same deposition period and deposition environment as the target strata are read, and the correlation between the pure mudstone GR data and the corresponding radioactive element content in the geological data of the known strata is analyzed, so as to establish a potential relationship model between the GR data in the known wells and the radioactive element content.
8. A method for recovering ancient water depth of a target stratum according to claim 1, characterized in that: The process of establishing the potential relationship model in step S5 is: using Excel to project the U element and the GR curve value into a plane coordinate map, selecting the fitting method with the highest correlation coefficient to obtain the empirical formula between the two, and establishing the conversion relationship between GR and U. The conversion relationship is the potential relationship model between the GR data and the radioactive element content.
9. A storage medium, characterized in that: The storage medium stores instructions and data for implementing a method for recovering ancient water depth of a target formation as described in any one of claims 1-8.
10. A target formation paleo-water depth recovery device, comprising a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a target formation paleo-water depth recovery method as described in any one of claims 1-8.