A dolostone lithology prediction method, system and apparatus

Abstract

The present application relates to the technical field of rock lithology exploration, and more particularly to a dolomite lithology prediction method, system and device; comprising the following steps: S1, obtaining drilled well data and seismic data of a region to be identified; S2, performing standardization processing; S3, performing well control feature frequency extraction; S4, performing fine production of a synthetic record for the region to be identified; S5, determining a seismic interpretation mode scheme for the region to be identified; S6, determining dolomite attributes; S7, obtaining a first content of dolomite in the region to be identified; S8, determining a second content of dolomite in the region to be identified; S9, determining whether the dolomite attributes determined in the step S7 are qualified according to the first content of dolomite and the second content of dolomite; and quantitatively predicting the content of dolomite in a carbonate rock formation.

Description

Technical Field

[0001] This invention relates to the field of rock lithology exploration technology, and in particular to a method, system and apparatus for predicting the lithology of dolomite. Background Technology

[0002] As exploration deepens, oil and gas exploration has gradually shifted from shallow to deep layers. The study of Ordovician dolomite reservoirs has always been a hot topic in oil and gas exploration. The development pattern of favorable and advantageous reservoirs in Ordovician dolomite reservoirs is mainly controlled by the evolution of ancient uplifts, fault activity, and fracture development. The overlapping areas of different karst processes have good dissolution conditions, and karst-fracture reservoirs are well-developed, making them high-quality reservoir development areas.

[0003] In some areas, large and thick carbonate rocks are developed. However, based on drilling data, oil and gas enrichment zones are mainly found in the dolomite reservoirs of the Ordovician Penglaiba Formation and Yingshan Formation. The genesis of karst reservoirs in other areas is particularly complex, and their characteristics and distribution patterns are unclear, increasing the difficulty in finding favorable dolomite reservoirs. Furthermore, the distribution areas of dolomite include dolomite-limestone facies transition zones, i.e., dolomite pinch-out lines. A dolomite pinch-out line is the boundary where dolomite strata gradually thin until they disappear. Within a specific geological region, due to changes in the sedimentary environment or tectonic movements, the distribution of dolomite may be limited to a certain extent. When dolomite strata extend in a certain direction, their thickness gradually decreases, eventually disappearing completely at a certain point. This transitional boundary is called the dolomite pinch-out line.

[0004] Because the sedimentary evolution of dolomite results in small differences in the physical parameters of limestone and dolomite, the seismic reflection characteristics of limestone and dolomite are not obvious, making it difficult to identify change points.

[0005] Therefore, there is an urgent need to provide a method, system, and device for predicting dolomite lithology, which can quantitatively predict the dolomite content in carbonate rock strata compared to existing technologies. Summary of the Invention

[0006] This invention addresses the technical problems existing in the prior art and provides a method, system, and apparatus for predicting dolomite lithology.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for predicting the lithology of dolomite includes the following steps:

[0009] S1. Obtain drilled well data and seismic data for the area to be identified;

[0010] S2. Standardize the drilled well data and seismic data obtained in step S1;

[0011] S3. Extract well control characteristic frequencies from the drilled and seismic data after the standardization process in step S2.

[0012] S4. Based on the extracted well control feature frequencies, perform detailed synthesis recording of the area to be identified;

[0013] S5. Based on the area to be identified, perform detailed synthesis of records and determine the seismic interpretation model scheme for the area to be identified;

[0014] S6. Determine the properties of dolomite based on the seismic interpretation model scheme for the area to be identified;

[0015] S7. Plot the well logging calcium carbonate and magnesium curves, use the post-stack impedance inversion method to perform cross-analysis of the well logging calcium carbonate and magnesium curves, and obtain the first content of dolomite in the area to be identified based on the properties of dolomite determined in step S6.

[0016] S8. Based on the magnesium carbonate content curve in the logging data, post-stack inversion is carried out to predict the lithology of dolomite, thereby determining the second content of dolomite in the area to be identified.

[0017] S9. Calculate the matching rate between the first content and the second content of dolomite. Based on the matching rate, determine whether the dolomite attribute determined in step S7 is qualified. If qualified, use the attribute to classify dolomite and limestone and determine the dolomite content in the area to be identified. If unqualified, repeat steps S1-S9 until qualified.

[0018] Furthermore, S4 specifically includes the following steps:

[0019] S41. Obtain the dominant frequency of the seismic wave in the well-controlled characteristic frequency, and set the dominant frequency in the synthetic record to be the same as the dominant frequency of the obtained seismic wave;

[0020] S42. Perform detailed synthesis recording of one of the individual wells;

[0021] S43. Using the methods in steps S41-S42, refine the synthetic record of the next single well until the refined synthetic record of all single wells is completed.

[0022] S44. Based on the detailed production of the synthetic records of all single wells, the detailed production of the synthetic records of the area to be identified is obtained.

[0023] Furthermore, in step S42, the Ricker wavelet method is used for preliminary calibration. After preliminary calibration, the correspondence between the synthetic record and the seismic data in the target layer is determined. Based on the correspondence, it is determined whether the Ricker wavelet method needs to be used again for determination. When no further determination is needed, the fine production of the synthetic record of the single well is completed.

[0024] Furthermore, the correspondence between the synthetic record and seismic data at the target layer is represented by correlation. A correlation threshold is set, and when the correlation is less than the correlation threshold, the Ricker wavelet method is used again for calibration.

[0025] Furthermore, the correlation is obtained by setting multiple sampling points in both the synthetic record and the seismic data, calculating the difference between the sampling points in the synthetic record and the corresponding sampling points in the seismic data, obtaining multiple calculated differences, and averaging the multiple calculated differences as the correlation.

[0026] Furthermore, in step S5, the detailed production of the synthetic record for the area to be identified is carried out through multiple rounds of single-well seismic fine calibration and multi-well seismic fine calibration to determine the seismic interpretation model scheme for the area to be identified.

[0027] Furthermore, seismic interpretation models include the stacking patterns, aggregate forms, distribution ranges, and spatial relationships of strata within a sequence.

[0028] Furthermore, in step S6, the seismic interpretation model scheme for the area to be identified is used to conduct test multi-attribute data for the area to be identified at different time windows, determine the actual multi-attribute data for the area to be identified, compare the test multi-attribute data with the actual multi-attribute data one by one, calculate the difference between the corresponding attribute data in the test multi-attribute data and the actual multi-attribute data, set the matching rate threshold, and set the attribute whose calculated difference is closest to the matching rate threshold as the attribute of dolomite.

[0029] Furthermore, the attribute types in both the test multi-attribute data and the actual multi-attribute data include amplitude, phase, frequency, energy, structure, and waveform classification.

[0030] Furthermore, in step S8, the specific method for conducting post-stack inversion prediction of dolomite lithology is as follows: a post-stack inversion model is set up, and each magnesium carbonate content curve in the logging data is taken as a sample. The sample is input into the post-stack inversion model, and the differences between the samples are used to drive sample selection and neural network learning. The post-stack inversion model is iteratively trained, and the number of iterations is set. When the number of iterations is reached, the iteration is stopped, and the trained post-stack inversion model is obtained. Based on the post-stack inversion model, the lithology of the dolomite in the area to be identified is obtained, thereby determining the second content of the dolomite in the area to be identified.

[0031] Furthermore, in step S9, a matching rate setting value is set. When the calculated matching rate is greater than the matching rate setting value, the properties of the dolomite confirmed in step S7 are deemed qualified; otherwise, they are deemed unqualified.

[0032] Furthermore, the matching rate in step S9 is calculated as follows: Matching rate = First content of dolomite divided by second content of dolomite, then multiplied by 100%.

[0033] A dolomite lithology prediction system includes a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, an eighth module, and a ninth module. The first module is used to execute the contents of step S1, the second module is used to execute the contents of step S2, the third module is used to execute the contents of step S3, the fourth module is used to execute the contents of step S4, the fifth module is used to execute the contents of step S5, the sixth module is used to execute the contents of step S6, the seventh module is used to execute the contents of step S7, the eighth module is used to execute the contents of step S8, and the ninth module is used to execute the contents of step S9.

[0034] A dolomite lithology prediction device includes a storage medium and a processor. The storage medium stores a computer program, and the processor is used to implement a dolomite lithology prediction method when executing the computer program.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] This invention is applicable to the quantitative characterization of dolomite content in carbonate rock strata. It uses the post-stack impedance inversion method to confirm whether the predicted dolomite properties are qualified, thereby improving the accuracy and efficiency of predicting dolomite lithology using post-stack seismic data. As a result, it can effectively and quantitatively predict the dolomite content in carbonate rock strata. Attached Figure Description

[0037] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0038] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0039] like Figure 1 As shown, this invention provides a method for predicting the lithology of dolomite, comprising the following steps:

[0040] S1. Obtain the drilled well data and seismic data of the area to be identified. The drilled well data includes well logging data and well logging data. The area to be identified is the Penglaiba Formation in the Yubei area.

[0041] S2. Standardize the drilled and seismic data obtained in step S1. Standardization includes outlier removal, wellbore environment correction, consistency processing, and fine standardization.

[0042] S3. Well control feature frequencies are extracted from the drilled and seismic data after the standardization process in step S2. The extracted well control feature frequencies are then processed to improve resolution and remove multiples. Existing resolution and multiple removal methods are used to improve the signal-to-noise ratio of the seismic data, highlight the differences in reflection anomalies of geological targets, and improve the identification accuracy of low-amplitude structures and stratigraphic lithological traps.

[0043] S4. Based on the well control feature frequencies processed in step S3, a refined synthetic record is created for the region to be identified, specifically including the following steps:

[0044] S41. Obtain the dominant frequency of the seismic wave from the processed well control characteristic frequency, and set the dominant frequency in the synthetic record to be the same as the dominant frequency of the obtained seismic wave.

[0045] S42. For one of the single wells, the Ricker wavelet method is used for preliminary calibration. After preliminary calibration, the correspondence between the synthetic record and the seismic data in the target layer is determined. The correspondence is represented by the correlation. A correlation threshold is set. When the correlation is less than the correlation threshold, the Ricker wavelet method is used again for calibration until the correlation is greater than or equal to the correlation threshold. The fine production of the synthetic record of the single well is then completed.

[0046] The correlation is preferably obtained by the following method: multiple sampling points are set in both the synthetic record and the seismic data. The difference between the sampling points in the synthetic record and the corresponding sampling points in the seismic data is calculated to obtain multiple calculated differences. The average value of the multiple calculated differences is recorded as the correlation.

[0047] S43. Using the methods in steps S41-S42, perform fine fabrication of the next single-well synthetic record until the fine fabrication of the synthetic records of all single wells is completed.

[0048] S44. For the detailed production of synthetic records of all single wells, conduct stratigraphic comparisons between multiple wells and geological formations to obtain detailed synthetic records of the area to be identified.

[0049] S5. Based on the fine production of the synthetic record of the area to be identified obtained in step S4, through multiple rounds of fine calibration of single-well seismic data and fine calibration of multi-well seismic data, the seismic interpretation model scheme of the area to be identified is determined. The seismic interpretation model scheme includes the stacking form, aggregate morphology, distribution range and internal spatial stacking relationship of the strata within the sequence.

[0050] S6. Based on the seismic interpretation model scheme for the area to be identified determined in step S5, conduct a seismic multi-attribute optimization study. Test multi-attribute data of the area to be identified are collected at different time windows. Based on the actual drilling and regional geological understanding, the actual multi-attribute data of the area to be identified is determined. The test multi-attribute data and the actual multi-attribute data are compared one by one. The difference between the corresponding attribute data in the test multi-attribute data and the actual multi-attribute data is calculated. A coincidence rate threshold is set. The attribute whose calculated difference is closest to the coincidence rate threshold is set as the attribute of dolomite. In this embodiment, the attribute with energy half-decay time of 0-100ms is the seismic attribute that qualitatively characterizes the lithology of dolomite.

[0051] The attribute types in both the test multi-attribute data and the actual multi-attribute data include amplitude, phase, frequency, energy, structure, and waveform classification.

[0052] S7. Plot well logging curves, well logging calcium carbonate and magnesium carbonate curves, and drilled lithology histograms of the area to be identified. Use the post-stack impedance inversion method to perform cross-analysis of the calcium carbonate and magnesium carbonate curves. Based on the dolomite properties determined in step S6, obtain the first content of dolomite in the area to be identified.

[0053] S8. Based on the magnesium carbonate content curves in the logging data, post-stack inversion is used to predict the lithology of dolomite. A post-stack inversion model is set up, and each magnesium carbonate content curve in the logging data is used as a sample. The sample is input into the post-stack inversion model, and the difference between the samples drives the sample selection and neural network learning to iteratively train the post-stack inversion model. The number of iterations is set, and the iteration is stopped when the number of iterations is reached. The trained post-stack inversion model is obtained, and the lithology of the dolomite in the area to be identified is obtained based on the post-stack inversion model, thereby determining the second content of the dolomite in the area to be identified.

[0054] S9. Calculate the first content of dolomite from step S7 and the second content of dolomite from step S8, calculate their matching rate, and set a matching rate setpoint. When the calculated matching rate is greater than the matching rate setpoint, the attribute of dolomite confirmed in step S7 is evaluated as qualified; otherwise, it is unqualified. The matching rate is calculated as follows: the first content of dolomite divided by the second content of dolomite and then multiplied by 100%. When the attribute of dolomite confirmed in step S7 is qualified, use this attribute to classify dolomite and limestone, and determine the content of dolomite in the area to be identified. When the attribute of dolomite confirmed in step S7 is unqualified, repeat steps S1-S9 until it is evaluated as qualified.

[0055] The present invention also provides a dolomite lithology prediction system, comprising a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, an eighth module, and a ninth module. The first module is used to execute the contents of step S1, the second module is used to execute the contents of step S2, the third module is used to execute the contents of step S3, the fourth module is used to execute the contents of step S4, the fifth module is used to execute the contents of step S5, the sixth module is used to execute the contents of step S6, the seventh module is used to execute the contents of step S7, the eighth module is used to execute the contents of step S8, and the ninth module is used to execute the contents of step S9.

[0056] The present invention also provides a dolomite lithology prediction device, including a storage medium and a processor. The storage medium stores a computer program, and the processor is used to implement the dolomite lithology prediction method provided by the present invention when executing the computer program.

[0057] This invention is applicable to the quantitative characterization of dolomite content in carbonate rock strata. It uses the post-stack impedance inversion method to confirm whether the predicted dolomite properties are qualified, thereby improving the accuracy and efficiency of predicting dolomite lithology using post-stack seismic data. As a result, it can effectively and quantitatively predict the dolomite content in carbonate rock strata.

[0058] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for predicting the lithology of dolomite, characterized in that, Includes the following steps: S1. Obtain drilled well data and seismic data for the area to be identified; S2. Standardize the drilled well data and seismic data obtained in step S1; S3. Extract well control characteristic frequencies from the drilled and seismic data after the standardization process in step S2. S4. Based on the extracted well control feature frequencies, perform detailed synthesis recording of the area to be identified; S5. Based on the area to be identified, perform detailed synthesis of records and determine the seismic interpretation model scheme for the area to be identified; S6. Determine the properties of dolomite based on the seismic interpretation model scheme for the area to be identified; S7. Plot the well logging calcium carbonate and magnesium curves, use the post-stack impedance inversion method to perform cross-analysis of the well logging calcium carbonate and magnesium curves, and obtain the first content of dolomite in the area to be identified based on the properties of dolomite determined in step S6. S8. Based on the magnesium carbonate content curve in the logging data, post-stack inversion is carried out to predict the lithology of dolomite, thereby determining the second content of dolomite in the area to be identified. S9. Calculate the matching rate between the first content and the second content of dolomite. Based on the matching rate, determine whether the dolomite attribute determined in step S7 is qualified. If qualified, use the attribute to classify dolomite and limestone and determine the dolomite content in the area to be identified. If unqualified, repeat steps S1-S9 until qualified.

2. The method for predicting dolomite lithology according to claim 1, characterized in that, S4 specifically includes the following steps: S41. Obtain the dominant frequency of the seismic wave in the well-controlled characteristic frequency, and set the dominant frequency in the synthetic record to be the same as the dominant frequency of the obtained seismic wave; S42. Perform detailed synthesis recording of one of the individual wells; S43. Using the methods in steps S41-S42, refine the synthetic record of the next single well until the refined synthetic record of all single wells is completed. S44. Based on the detailed production of the synthetic records of all single wells, the detailed production of the synthetic records of the area to be identified is obtained.

3. The method for predicting the lithology of dolomite according to claim 2, characterized in that, In step S42, the Ricker wavelet method is used for preliminary calibration. After preliminary calibration, the correspondence between the synthetic record and the seismic data in the target layer is determined. Based on the correspondence, it is determined whether the Ricker wavelet method needs to be used again for determination. When no further determination is needed, the fine production of the synthetic record for this single well is completed.

4. The dolomite lithology prediction method according to claim 3, characterized in that, The correspondence between synthetic records and seismic data at the target layer is represented by correlation. A correlation threshold is set, and when the correlation is less than the correlation threshold, the Ricker wavelet method is used again for calibration.

5. The dolomite lithology prediction method according to claim 4, characterized in that, The correlation is obtained by setting multiple sampling points in both the synthetic record and the seismic data. The difference between the sampling points in the synthetic record and the corresponding sampling points in the seismic data is calculated to obtain multiple calculated differences. The average of the multiple calculated differences is taken as the correlation.

6. The method for predicting the lithology of dolomite according to claim 1, characterized in that, In step S5, the synthetic record of the area to be identified is meticulously prepared through multiple rounds of single-well seismic fine calibration and multi-well seismic fine calibration to determine the seismic interpretation model scheme for the area to be identified.

7. The method for predicting the lithology of dolomite according to claim 6, characterized in that, Seismic interpretation models include the stacking patterns, aggregate forms, distribution ranges, and spatial relationships of strata within a sequence.

8. The method for predicting the lithology of dolomite according to claim 1, characterized in that, In step S6, the seismic interpretation model scheme for the area to be identified is determined, test multi-attribute data for the area to be identified are conducted at different time windows, the actual multi-attribute data for the area to be identified is determined, the test multi-attribute data and the actual multi-attribute data are compared one by one, the difference between the corresponding attribute data in the test multi-attribute data and the actual multi-attribute data is calculated, the coincidence rate threshold is set, and the attribute whose calculated difference is closest to the coincidence rate threshold is set as the attribute of dolomite.

9. The method for predicting the lithology of dolomite according to claim 8, characterized in that, The attribute types in both the test multi-attribute data and the actual multi-attribute data include amplitude, phase, frequency, energy, structure, and waveform classification.

10. The dolomite lithology prediction method according to claim 1, characterized in that, In step S8, the specific method for conducting post-stack inversion prediction of dolomite lithology is as follows: a post-stack inversion model is set up, and each magnesium carbonate content curve in the logging data is taken as a sample. The sample is input into the post-stack inversion model, and the differences between the samples are used to drive sample selection and neural network learning. The post-stack inversion model is iteratively trained, and the number of iterations is set. When the number of iterations is reached, the iteration is stopped, and the trained post-stack inversion model is obtained. Based on the post-stack inversion model, the lithology of the dolomite in the area to be identified is obtained, thereby determining the second content of the dolomite in the area to be identified.

11. The dolomite lithology prediction method according to claim 1, characterized in that, In step S9, a matching rate setting value is set. When the calculated matching rate is greater than the matching rate setting value, the properties of the dolomite confirmed in step S7 are deemed qualified; otherwise, they are deemed unqualified.

12. The dolomite lithology prediction method according to claim 11, characterized in that, The matching rate in step S9 is calculated as follows: Matching rate = (first content of dolomite divided by second content of dolomite) multiplied by 100%.

13. A dolomite lithology prediction system, characterized in that, The method for predicting dolomite lithology according to any one of claims 1-12 includes a first module, a second module, a third module, a fourth module, a fifth module, a sixth module, a seventh module, an eighth module, and a ninth module. The first module is used to execute the contents of step S1, the second module is used to execute the contents of step S2, the third module is used to execute the contents of step S3, the fourth module is used to execute the contents of step S4, the fifth module is used to execute the contents of step S5, the sixth module is used to execute the contents of step S6, the seventh module is used to execute the contents of step S7, the eighth module is used to execute the contents of step S8, and the ninth module is used to execute the contents of step S9.

14. A dolomite lithology prediction device, characterized in that, It includes a storage medium and a processor, wherein the storage medium stores a computer program, and the processor is used to implement the dolomite lithology prediction method according to any one of claims 1-12 when executing the computer program.

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