A quantitative evaluation method for the potential resources of oil field development

Abstract

The present invention relates to the technical field of quantitative analysis of oilfield resources, and in particular to a quantitative evaluation method for the amount of resources with oilfield development potential. The method comprises the following steps: obtaining a geophysical exploration data set; performing data preprocessing on the geophysical exploration data set to generate a geophysical exploration standard data set; constructing a three-dimensional geological model based on the geophysical exploration standard data set to generate a three-dimensional underground oilfield reserve model; obtaining real-time formation measurement data; using the three-dimensional underground oilfield reserve model to predict underground oilfields based on the real-time formation measurement data to generate an oilfield development potential prediction result; generating a development potential report based on the oilfield development potential prediction result to generate an oilfield development potential report; therefore, the present invention solves the problem of lack of real-time and accuracy in traditional oilfield assessment by realizing dynamic monitoring and updating of oilfield development potential, and improves the resource utilization efficiency of the oilfield development process.

Description

Technical Field

[0001] The invention relates to the technical field of quantitative analysis of oilfield resources, and in particular to a quantitative evaluation method for the potential resource quantity of oilfield development. Background Art

[0002] Traditional exploration data collection relies on limited well locations or exploration holes, resulting in the lack of spatial representation of the geological and oil and gas resource data obtained, which cannot accurately reflect the comprehensive spatial distribution and complex geological heterogeneity of the oil field. Due to the high spatial variability in the oil field development process, traditional methods are difficult to accurately assess the oil and gas reserves and development potential in different regions, which often leads to deviations in resource assessment. Existing technologies generally ignore the impact of dynamic factors on the development potential of oil fields. Oil field development is a long-term and dynamically changing process. Traditional quantitative evaluation methods are mostly based on static geological data and initial oil field conditions for estimation, and fail to fully consider the changes in oil field reserves and mobility caused by factors such as mining strategies, development progress, and water injection during oil field development. This makes the prediction accuracy and applicability of traditional methods in actual development poor. Traditional methods ignore key technologies such as multidimensional data fusion and spatiotemporal error correction. Quantitative evaluation of oilfield resources requires comprehensive analysis of multi-dimensional information including seismic exploration data, downhole pressure, temperature, porosity, permeability, etc. However, existing technologies have certain limitations in the integration and processing of these data, and fail to fully utilize advanced statistical methods and simulation techniques to perform spatiotemporal dynamic modeling and multi-point simulation of complex data, resulting in insufficient accuracy of evaluation results. Summary of the invention

[0003] Based on this, it is necessary to provide a quantitative evaluation method for the potential resource volume of oil field development to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for quantitatively evaluating the potential resources of oil field development is provided, the method comprising the following steps: Step S1: obtaining a geophysical exploration data set; performing data preprocessing on the geophysical exploration data set to generate a geophysical exploration standard data set; Step S2: constructing a three-dimensional geological model based on a geophysical exploration standard data set to generate a three-dimensional underground oil field reserve model; Step S3: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating an oil field development potential prediction result; generating a development potential report based on the oil field development potential prediction result, and generating an oil field development potential report; Step S4: measure underground oilfield exploration parameters according to the oilfield development potential report to generate oilfield exploration parameter data; update the geophysical exploration data set based on the oilfield exploration parameter data, thereby completing the quantitative operation of oilfield development potential resources.

[0005] The beneficial effect of the present invention is that, by obtaining geophysical exploration data and preprocessing, the quality of input data is ensured, noise and inconsistency are eliminated, thereby providing a reliable basis for the subsequent construction of a three-dimensional geological model. By generating a standardized data set, not only the uniformity of the data is ensured, but also data support is provided for further spatial distribution modeling. In step S2, a three-dimensional geological model is constructed based on a geophysical exploration standard data set, providing an accurate geological background for the estimation of underground oil field reserves. The model realizes a refined assessment of reserves by integrating physical parameters such as porosity, permeability, and lithology. Step S3 predicts the real-time measurement data using the generated reserve model, updates the prediction results of oil field development potential in combination with real-time data, and provides a scientific basis for development decisions. By generating an oil field development potential report, data support is further provided for the optimization of the development process. Step S4 measures the oil field exploration parameters according to the development potential report, and updates the geophysical exploration data set, so that the oil field development potential resource assessment continues to track the actual development situation, and can adjust the assessment model and development strategy in time. This full-process data integration and optimization mechanism effectively improves the accuracy and real-time performance of oilfield resource assessment, and provides reliable data support for the sustainable development of oilfields. Therefore, the present invention solves the problem of lack of real-time performance and accuracy in traditional oilfield assessment by realizing dynamic monitoring and updating of oilfield development potential, and improves the resource utilization efficiency of the oilfield development process.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire a geophysical exploration data set, wherein the geophysical exploration data set includes stratigraphic data, rock property data and geophysical exploration data; Step S12: removing outliers from the formation data to generate formation data removal data; performing porosity temperature correction on the rock property data to generate rock porosity correction data; performing permeability pressure adjustment on the rock porosity correction data to generate rock property processing data; Step S13: performing medium space registration on the geophysical exploration data to generate geogeological processing data; Step S14: Perform spatial resampling standard format consistency processing on the stratigraphic data removal data, rock porosity correction data, rock property processing data and earth geology processing data to generate a geophysical exploration standard data set.

[0007] The present invention lays a foundation for oilfield resource assessment by acquiring a comprehensive data set including formation data, rock properties and geophysical exploration data. By removing outliers from the formation data, the accuracy and effectiveness of the data are ensured, the existing errors and noise are eliminated, and the deviations in subsequent analysis are avoided. In addition, by performing temperature correction on the rock porosity data, the reliability of the porosity data is further improved, so that it can more accurately reflect the actual underground rock characteristics; then, the pressure regulation treatment of the permeability corrects the potential errors in the rock porosity data, ensuring the true reflection of the permeability data, making the reservoir physical property parameters more accurate. The geophysical exploration data is subjected to medium space registration, which ensures the consistency of the spatial position of the data from different exploration methods and eliminates the data deviation caused by different detection methods. This process provides an accurate data basis for building a unified three-dimensional geological model and reserve assessment through precise spatial registration. Through spatial resampling and standard format consistency processing, multiple data sources (stratigraphic data, rock property data, geophysical data, etc.) are integrated into a data set in a unified format. This data standardization process ensures the compatibility and consistency of the data during subsequent analysis and model building, thereby avoiding processing errors or reduced accuracy caused by inconsistent data formats.

[0008] Preferably, step S13 includes the following steps: Step S131: using seismic exploration technology to obtain geological structure reflection wave data from stratum data to generate underground geological reflection wave data; Step S132: performing high-frequency noise processing on the underground geological reflection wave data to generate underground geological noise reduction data; using tomography technology to perform wave propagation time and space error correction on the underground geological noise reduction data to generate underground geological reflection wave processing data; Step S133: acquiring resistivity information of different layers from the stratum data to generate underground layer resistivity information data; eliminating the non-uniformity error of the underground layer resistivity information data to generate underground geological resistivity processing data; Step S134: performing medium space registration on the underground geological reflection wave processing data and the underground geological resistivity processing data to generate earth geological processing data.

[0009] The present invention obtains the reflected wave data of the geological structure by using seismic exploration technology, and provides important spatial information for the construction of subsequent geological models. These reflected wave data can reflect the physical characteristics of the underground geological structure. By analyzing the propagation characteristics of the reflected wave, the physical properties of different underground strata can be accurately inferred. However, the reflected wave data is interfered by high-frequency noise, which will affect the accuracy of subsequent analysis. The underground geological reflected wave data is subjected to high-frequency noise processing to remove unnecessary interference information and generate noise-reduced data, thereby improving the quality and signal-to-noise ratio of the data. Further, the tomography technology is used to correct the wave propagation time and space errors of these noise-reduced data, which can effectively eliminate the influence caused by the time and space errors in the exploration process, ensure the accuracy of the data, and then provide more accurate underground geological reflected wave data. The resistivity information data of different underground layers are obtained by means of georesistivity detection. These data help to deeply understand the electrical characteristics of different underground layers, and provide an important basis for the identification and analysis of oil and gas reservoirs. Resistivity data is often affected by underground heterogeneity and geological complexity, so it is necessary to eliminate the heterogeneity error during the processing process, and more reliable underground geological resistivity processing data is generated through this error elimination. The underground geological reflection wave processing data and resistivity processing data are registered in the medium space, which realizes the spatial unification of different physical data, eliminates the spatial position error caused by the difference in data sources, makes the final geogeological processing data more accurate, and can accurately present the structural characteristics of underground geology in three-dimensional space. This series of data processing processes systematically removes interference factors, improves the accuracy and consistency of data, and provides more refined and reliable data support for oilfield resource assessment, geological model construction and development decision-making.

[0010] Preferably, step S2 comprises the following steps: Step S21: Performing oil and gas reservoir sequence stratigraphic analysis on underground geological reflection wave processing data, and obtaining oil and gas reservoir characteristic data of source oil fields; Step S22: performing seismic inversion on the underground oilfield oil and gas reservoir prediction data to generate underground oilfield oil and gas reservoir data; Step S23: Perform three-dimensional modeling using underground oil field oil and gas reservoir data and geophysical exploration standard data sets to generate a three-dimensional underground oil field reserve model.

[0011] The present invention improves the prediction accuracy of underground oilfield oil and gas reservoirs through a series of sophisticated data processing and modeling steps, and provides more scientific and reliable data support for the quantitative evaluation of oilfield resources. By performing oil and gas reservoir sequence stratigraphic analysis on underground geological reflection wave processing data, the sequence and distribution characteristics of oil and gas reservoirs can be identified. This process reveals the spatial distribution pattern of underground oil and gas reservoirs through the analysis of reflection wave data, thereby providing a reliable basis for subsequent oil and gas reservoir prediction. This analysis not only helps to identify the boundaries of oil and gas reservoirs, but also reveals the storage space structure of oil and gas reservoirs, providing basic data for the evaluation of oil and gas resources. By performing seismic inversion on underground oilfield oil and gas reservoir prediction data and using the inversion technology of seismic reflection wave data, the physical properties (such as porosity, permeability, etc.) of underground oil and gas reservoirs can be more accurately calculated. Seismic inversion technology converts observed seismic wave reflection information into underground physical property data, effectively making up for the underground part that cannot be covered in direct drilling and experiments, and providing a more accurate quantitative description of the reserves, distribution and characteristics of oil and gas reservoirs. Through the oil and gas reservoir data after seismic inversion, oilfield developers can obtain more detailed geological information to support the formulation of subsequent development decisions. Combined with underground oilfield oil and gas reservoir data and geophysical exploration standard data sets, three-dimensional modeling is performed to generate a three-dimensional underground oilfield reserve model. Through three-dimensional modeling, the geological characteristics and reserve distribution of oil and gas reservoirs can be accurately presented in space. This three-dimensional model can not only reflect the three-dimensional structure of the oil and gas reservoir, but also quantify the amount of resources under different geological conditions, further improving the accuracy of oil and gas resource assessment. This complete set of processing procedures, through precise data analysis and modeling, overcomes the problems of insufficient data and accuracy deviation in traditional exploration, and provides a more comprehensive and efficient solution for the scientific development of oilfield resources.

[0012] Preferably, step S23 includes the following steps: Step S231: constructing a primary model position model using underground oilfield oil and gas reservoir data and underground geological resistivity processing data to generate an underground oilfield primary position model; Step S232: using the formation data to remove data, the physical property parameters of the primary location model of the underground oil field are quantitatively processed to generate a first-layer model of physical property quantification; Step S233: using the rock porosity correction data and the rock property processing data to qualitatively characterize the acoustic wave parameters of the lithology storage layer of the primary location model of the underground oil field, and generating a lithology quantitative second layer model; Step S234: spatially discretize the first-layer quantitative physical property model and the second-layer quantitative lithology model to grid, and generate an underground oil field grid model; perform multi-point geological simulation on the underground oil field grid model to generate an underground oil field multi-point simulation model; perform reserve interpolation on the underground oil field multi-point simulation model to generate a three-dimensional underground oil field reserve model.

[0013] The present invention constructs a primary location model by using underground oil field oil and gas reservoir data and underground geological resistivity processing data. This process realizes the preliminary determination of the spatial location of the underground oil field reservoir by combining the geological reflection wave characteristics and resistivity information of the oil and gas reservoir. Resistivity data can reflect the electrical characteristics of underground rocks and provide an important basis for determining the strata and boundaries of oil and gas reservoirs, while oil and gas reservoir data reveals the spatial structure of underground reservoirs through seismic wave reflection and other methods. Through the combination of the two, the primary location model of the underground oil field can be constructed more accurately, providing basic data for subsequent resource assessment and development planning. The physical property parameters of the primary location model of the underground oil field are quantitatively processed by using the formation data removal data to generate a physical property quantitative first layer model. Physical property parameters (such as porosity, permeability, etc.) are key factors in evaluating the reserves of oil and gas reservoirs. After the formation data removal data is processed, outliers and noise can be eliminated to ensure the accuracy of the physical property parameters, thereby improving the credibility of the model. Through this processing step, an accurate physical property parameter model is obtained, which provides key data support for reservoir characteristic analysis. The rock porosity correction data and rock property processing data are used to qualitatively process the lithology parameters of the primary location model of the underground oil field, and generate a second-layer lithology quantitative model. Lithology parameters can further characterize the pore structure and permeability characteristics of the reservoir. The introduction of porosity correction and lithology processing data makes the distribution of lithology characteristics in space more consistent with the actual underground situation, thereby providing more detailed and accurate data support for subsequent reserve assessment and development planning. The physical property quantitative first-layer model and the lithology quantitative second-layer model are spatially discretized and gridded to generate an underground oil field grid model. This step can calculate the attributes of the reservoir at a finer spatial scale by dividing the model into regular grid units, thereby improving the accuracy of reserve estimation. In addition, through the application of multi-point geological simulation, the limitations of single assumptions in traditional modeling can be further reduced, and a multi-point simulation model of the underground oil field can be generated. This method improves the diversity and accuracy of the oil field reserve model by introducing a variety of geological scenarios. Finally, through the process of reserve interpolation, the data of the multi-point simulation model is converted into a reserve model that is more in line with the actual oil field development potential, providing accurate data support for oil field development decisions.

[0014] Preferably, step S232 includes the following steps: Step S2321: using formation data to remove data, perform core experiments to generate formation core data; perform mercury injection porosity measurement on the formation core data to generate formation porosity physical property parameter data; Step S2322: performing reflection amplitude correlation analysis on the formation porosity physical parameter data and the underground geological reflection wave processing data, and constructing a porosity spatial distribution layer to generate an oilfield porosity spatial distribution layer; Step S2323: Calculate the oil and gas mobility of the formation data removal data to generate preliminary data of underground oil field permeability; perform core permeability test on the preliminary data of underground oil field permeability to generate underground oil field permeability test data; construct a permeability distribution layer according to the underground oil field oil and gas reservoir data and the underground oil field permeability test data to generate an underground oil field permeability distribution layer; Step S2324: perform physical property parameter spatial modeling on the primary location model of the underground oil field using the oil field porosity spatial distribution layer and the underground oil field permeability distribution layer to generate a first-layer model of physical property quantitativeness.

[0015] The present invention removes data from formation data and conducts core experiments to obtain formation core data, and then obtains formation porosity physical parameter data through mercury injection porosity measurement. Porosity is one of the key physical parameters of oil and gas reservoirs, reflecting the size and volume of pore space in rocks. Through mercury injection porosity measurement technology, more accurate porosity data is obtained, which provides a scientific basis for subsequent oil and gas resource assessment and development planning. By performing reflection amplitude correlation analysis on formation porosity physical parameter data and underground geological reflection wave processing data, combined with the spatial distribution information of porosity, the porosity spatial distribution layer of the oil field is constructed. The geological reflection wave data reflects the reflection characteristics of the underground reservoir. The relationship between the change of porosity and the reflection amplitude can be revealed through correlation analysis, further providing an accurate construction basis for the spatial distribution layer of porosity. This step improves the accuracy of reservoir characteristic description by accurately constructing the porosity spatial distribution layer, and lays the foundation for further oil and gas resource assessment. The formation data was removed to calculate the fluidity of oil and gas, generate preliminary data of underground oil field permeability, and core permeability test was performed on these data, and finally the underground oil field permeability test data was obtained. Permeability is an important parameter affecting oil and gas fluidity and production efficiency. The ability to accurately grasp the spatial distribution of permeability is of great significance for evaluating the production potential of oil fields. Subsequently, the construction of the permeability distribution layer was completed by combining the underground oil field oil and gas reservoir data and the permeability test data. In this process, the spatial distribution information of permeability was fully extracted, which provided support for the fluid kinetic energy analysis and production plan design of the oil field. The spatial distribution layer of oil field porosity and the underground oil field permeability distribution layer were combined to perform spatial modeling of physical property parameters for the primary location model of the underground oil field, and a first-layer model of physical property quantitative was generated. Through the first-layer model of physical property quantitative, the changes in the porosity and permeability of the oil field can be accurately reflected in space, thereby providing accurate physical property data support for the quantitative evaluation of oil field reserves. This complete set of physical property parameter modeling methods solves the problems of insufficient data and rough models in traditional oil field exploration through precise experimental data and spatial analysis, and improves the accuracy of assessment of oil field development potential.

[0016] Preferably, step S233 includes the following steps: Step S2331: performing core analysis on the rock property processing data to generate underground oil field core analysis data; Step S2332: Perform storage layer regression correction on the underground oil field core analysis data using the rock porosity correction data to generate underground oil field storage regression data; perform acoustic wave time difference anomaly detection on the underground oil field storage regression data, and remove outliers to generate underground oil field storage layer acoustic wave data; Step S2333: Use underground geological resistivity processing data to perform core resistivity testing on underground oil field core analysis data, and combine underground oil field storage layer acoustic wave data to qualitatively characterize the lithology storage layer acoustic wave parameters of the underground oil field primary location model to generate a lithology quantitative second layer model.

[0017] The present invention uses rock property processing data to perform core analysis, and extracts representative core data from it. These data are crucial to the description of underground reservoirs and can provide information about rock porosity, lithology, mineral composition, etc. Core analysis can more directly reveal the actual physical and chemical properties of rocks, and provide basic data for the accurate modeling of oil field physical property parameters. The rock porosity correction data and the underground oil field core analysis data are further subjected to storage layer regression correction. This process corrects the error of the core data through the regression model, so that the data is more in line with the actual underground reservoir conditions. At the same time, the underground oil field storage regression data is subjected to acoustic wave time difference abnormal detection, and the detected abnormal values ​​are eliminated, thereby eliminating the noise and error in the data and generating more accurate underground oil field storage layer acoustic wave data. Acoustic wave time difference data can provide reservoir elastic properties in oil and gas reservoirs, and further help analyze key physical parameters such as fluid saturation state and pore structure of rocks. Through this series of steps, the accuracy and consistency of rock porosity and acoustic wave data are ensured, laying a solid foundation for the next step of lithology parameter modeling. The core analysis data of underground oil fields is combined with the underground geological resistivity processing data to conduct core resistivity testing, and further qualitative analysis of the lithology parameters of the primary location model of the underground oil fields is conducted. Through the test of core resistivity, combined with the acoustic data and regression correction data, a more accurate lithology quantitative second-layer model is constructed. In this process, the resistivity data provides the conductivity information of the rock, which can reflect the pore structure and fluid distribution characteristics of the reservoir, thereby more accurately describing the lithology characteristics of the underground oil fields. Through these steps, the entire physical parameter modeling process has been effectively improved, especially in the quantitative description of lithology and porosity, making the prediction of oil field development potential more scientific and accurate.

[0018] Preferably, step S3 comprises the following steps: Step S31: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating prediction results for oil field development potential; Step S32: performing spatial heterogeneity analysis on the oilfield development potential prediction results to generate spatial uncertainty analysis data for underground oilfields; Step S33: Generate an oilfield development potential report based on the spatial uncertainty analysis data of the underground oilfield.

[0019] The present invention obtains real-time formation measurement data and combines it with a three-dimensional underground oil field reserve model to perform oil field prediction. This process can dynamically capture the actual changes in the oil field and accurately predict the development potential of the oil field by applying the real-time measurement data to the existing reserve model. This step updates the oil field reserve model through real-time data, provides a more accurate prediction of development potential, and provides data support for subsequent oil field development decisions. Then, spatial heterogeneity analysis is further performed to generate spatial uncertainty analysis data of the underground oil field. Spatial heterogeneity analysis can effectively evaluate the complexity and uncertainty of the underground reservoir by exploring the differences in physical properties of each geological layer in the oil field and its spatial heterogeneity, thereby ensuring the reliability of the development potential evaluation results. In this process, the spatial variability of the oil and gas reservoir is analyzed in combination with the oil field reserve model and real-time measurement data, providing a quantitative basis for uncertainty. Based on the above spatial uncertainty analysis data, an oil field development potential report is generated, which provides scientific, systematic and detailed analysis results for oil field development by quantitatively describing the spatial uncertainty, potential estimation and development strategy of the oil field. This type of report not only provides an assessment of oil field resources, but also helps decision makers understand the potential risks and challenges of oil field development, thereby formulating more scientific development plans.

[0020] Preferably, step S32 includes the following steps: Step S321: Perform lithology and porosity analysis on the oilfield development potential prediction results to generate oilfield location attribute analysis data; perform multi-point simulation on the oilfield location attribute analysis data to generate oilfield location attribute simulation data; Step S322: Eliminate lithologic heterogeneity from the oilfield location attribute simulation data to generate oilfield location prediction elimination data; perform exploration data error analysis based on the oilfield location prediction elimination data to generate oilfield exploration location data; Step S323: Perform spatial heterogeneity analysis on the oilfield exploration location data to generate underground oilfield spatial uncertainty analysis data.

[0021] The present invention generates oilfield location attribute analysis data by performing lithology and porosity analysis on the prediction results of oilfield development potential. This process deeply analyzes the different lithology, porosity characteristics and spatial distribution in the oilfield area, helps to scientifically understand the physical characteristics of the reservoir and the spatial structure of the oil and gas reservoir, and thus provides more accurate basic data for the evaluation of oilfield potential. Subsequently, a multi-point simulation method is used to simulate the oilfield location attribute analysis data to generate oilfield location attribute simulation data. This link uses a multi-point simulation method to perform spatial statistical simulation on different locations of the oilfield, thereby obtaining a more refined oilfield location attribute change trend, and can capture the potential spatial variability in the oilfield, providing solid data support for subsequent development potential evaluation. The oilfield location attribute simulation data is subjected to lithology heterogeneity elimination to generate oilfield location prediction elimination data, and further exploration data error analysis is performed based on these data to generate oilfield exploration location data. Through this process, the interference caused by lithology heterogeneity is effectively eliminated, the prediction error caused by factors such as lithology differences and porosity changes is solved, and the accuracy and consistency of subsequent oilfield development potential evaluation data are ensured. Spatial heterogeneity analysis of oilfield exploration location data was performed to generate spatial uncertainty analysis data for underground oilfields. The Monte Carlo simulation method can provide uncertainty range assessment for the spatial distribution of underground oilfields through multiple random simulations and statistical analysis, clarify the development potential of oilfields under different scenarios, and provide scientific risk assessment and decision-making basis for development decisions. This series of steps eliminates uncertainty and errors in the data by comprehensively analyzing the geology, physical properties and spatial distribution of oilfields from multiple dimensions, and ultimately obtains reliable development potential assessment results, which significantly improves the scientificity and accuracy of oilfield development decisions.

[0022] Preferably, step S4 comprises the following steps: Step S41: measuring underground oil field survey parameters according to the oil field development potential report to generate oil field survey parameter data; Step S42: abnormal fluctuation detection is performed on the oilfield survey parameter data, and abnormal fluctuation elimination is performed to generate oilfield survey selection data; Step S43: updating the geophysical exploration data set based on the oilfield survey and selection data, thereby completing the quantitative operation of oilfield development potential resources.

[0023] The present invention generates oilfield exploration parameter data by measuring underground oilfield exploration parameters according to the oilfield development potential report. This process obtains first-hand data by accurately measuring the key exploration parameters of the oilfield (such as physical properties such as porosity, permeability, and lithology) and according to the information of the oilfield development potential report, providing a basis for further quantitative analysis and prediction. The accurate measurement of these exploration parameters ensures that the subsequent quantitative resource assessment can be carried out under a scientific and practical geological background. The oilfield exploration parameter data is detected for abnormal fluctuations, and the abnormal fluctuations are eliminated to generate oilfield exploration selection data. Abnormal fluctuation detection can identify unreasonable fluctuations and potential interference factors in the data. If these abnormal data are not eliminated, they will cause large errors in resource estimation, thereby affecting the final development decision. Therefore, eliminating abnormal fluctuations is a key step to ensure data quality, which helps to improve the reliability and accuracy of oilfield exploration parameters. The geophysical exploration data set is updated based on the oilfield exploration selection data, thereby completing the quantitative operation of oilfield development potential resources. Through the processing and application of oilfield exploration and selection data, the geophysical exploration data set is further optimized to make it more in line with the needs of actual oilfield development. The data update not only improves the accuracy of resource estimation, but also provides an accurate basis for future oilfield development strategies and management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a quantitative evaluation method for the potential resource of oil field development; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 4 , a method for quantitatively evaluating the potential resources of oil field development, the method comprising the following steps: Step S1: obtaining a geophysical exploration data set; performing data preprocessing on the geophysical exploration data set to generate a geophysical exploration standard data set; Step S2: constructing a three-dimensional geological model based on a geophysical exploration standard data set to generate a three-dimensional underground oil field reserve model; Step S3: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating an oil field development potential prediction result; generating a development potential report based on the oil field development potential prediction result, and generating an oil field development potential report; Step S4: measure underground oilfield exploration parameters according to the oilfield development potential report to generate oilfield exploration parameter data; update the geophysical exploration data set based on the oilfield exploration parameter data, thereby completing the quantitative operation of oilfield development potential resources.

[0029] The beneficial effect of the present invention is that, by obtaining geophysical exploration data and preprocessing, the quality of input data is ensured, noise and inconsistency are eliminated, thereby providing a reliable basis for the subsequent construction of a three-dimensional geological model. By generating a standardized data set, not only the uniformity of the data is ensured, but also data support is provided for further spatial distribution modeling. In step S2, a three-dimensional geological model is constructed based on a geophysical exploration standard data set, providing an accurate geological background for the estimation of underground oil field reserves. The model realizes a refined assessment of reserves by integrating physical parameters such as porosity, permeability, and lithology. Step S3 predicts the real-time measurement data using the generated reserve model, updates the prediction results of oil field development potential in combination with real-time data, and provides a scientific basis for development decisions. By generating an oil field development potential report, data support is further provided for the optimization of the development process. Step S4 measures the oil field exploration parameters according to the development potential report, and updates the geophysical exploration data set, so that the oil field development potential resource assessment continues to track the actual development situation, and can adjust the assessment model and development strategy in time. This full-process data integration and optimization mechanism effectively improves the accuracy and real-time performance of oilfield resource assessment, and provides reliable data support for the sustainable development of oilfields. Therefore, the present invention solves the problem of lack of real-time performance and accuracy in traditional oilfield assessment by realizing dynamic monitoring and updating of oilfield development potential, and improves the resource utilization efficiency of the oilfield development process.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a quantitative evaluation method for the potential resources of oil field development according to the present invention. In this example, the quantitative evaluation method for the potential resources of oil field development includes the following steps: Step S1: obtaining a geophysical exploration data set; performing data preprocessing on the geophysical exploration data set to generate a geophysical exploration standard data set; In the embodiment of the present invention, data denoising is performed, and high-frequency noise and instrument errors in the data are removed by using filters, signal processing methods and other technologies. This can remove non-target signals by time window filtering, Fourier transform and other methods to ensure the clarity and accuracy of the data. The reflected wave signal in the seismic exploration data is processed, enhanced and dedistorted to ensure the clarity of the reflected signal and optimize the extraction accuracy of the underground structure information. Furthermore, coordinate conversion and spatial registration are performed to ensure that the data from different exploration methods are consistent and comparable in space, for example, the data of different detection depths or measurement positions are unified into the same coordinate system. At the same time, it is also necessary to unify and standardize the data formats of the data obtained by different geophysical exploration methods so that the data from different instruments can be compatible and docked, eliminating the problem of inconsistent data formats caused by instrument differences or different acquisition methods. In the process of data preprocessing, missing value processing is often required. In the process of data acquisition, due to instrument failure, acquisition error or incomplete measurement, data missing may occur. Missing values ​​can be filled by interpolation methods (such as linear interpolation, Kriging interpolation, etc.) to ensure the integrity of the data set. In the case of outliers, anomaly detection and correction are also required to avoid the overall analysis results being affected by extreme or unreasonable values.

[0031] Step S2: constructing a three-dimensional geological model based on a geophysical exploration standard data set to generate a three-dimensional underground oil field reserve model; In the embodiment of the present invention, the construction of the three-dimensional geological model aims to construct a three-dimensional model of the underground oil field through the precise integration of spatial information and the description of the spatial distribution of physical parameters, so as to provide a scientific basis for the calculation of oil field reserves and development planning. The specific technical means are firstly the spatial interpolation and gridding of data. By using spatial interpolation methods such as Kriging or inverse distance weighting (IDW), the discrete point data in the geophysical exploration standard data set is converted into a continuous three-dimensional spatial distribution. These methods infer the data values ​​of unknown positions based on the spatial relationship of known data points to ensure the smooth transition of spatial information and the coherence of data. Based on the physical property data such as strata, lithology, porosity, permeability, etc. in the standard data set, the physical property parameter model of each layer is constructed. Core data, downhole measurement data and seismic inversion results are used to describe the spatial distribution of these physical property parameters. Through multi-point geological simulation technology, statistical methods are used to simulate the changes of physical property parameters under different situations to generate a multi-level physical property model. The quantitative processing of physical parameters includes the spatial assignment of important geological parameters such as lithology, porosity, and permeability, thereby forming an underground reservoir model with a higher resolution. In addition, the three-dimensional modeling process also needs to deal with complex geological structural factors such as faults, folds, groundwater flow paths, etc., and integrate data from different sources such as seismic data, georesistivity data, and downhole measurement data to ensure that the model can reflect the actual underground oil and gas reservoir conditions. Through the above-mentioned three-dimensional modeling technology, the underground geological body is divided into multiple grid units, and the lithology type, porosity, permeability and other attributes of the reservoir are defined in each grid unit, and the oil and gas reserves of the reservoir are quantified. In order to further ensure the accuracy and reliability of the model, uncertainty analysis methods such as Monte Carlo simulation can also be used to quantify the uncertainty in the three-dimensional geological model, so as to obtain a more accurate estimate of the oil field reserves.

[0032] Step S3: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating an oil field development potential prediction result; generating a development potential report based on the oil field development potential prediction result, and generating an oil field development potential report; In the embodiment of the present invention, real-time formation measurement data is obtained, and underground physical parameters in the oil field are collected in real time through downhole sensors, seismic reflection data or formation pressure, temperature, permeability and other measurement technologies. These real-time measurement data include but are not limited to the three-phase flow of gas, oil and water, porosity, permeability, lithology distribution, pressure, temperature and the like, which directly affect the effectiveness and feasibility of oil field development. The acquisition of real-time data requires advanced downhole equipment and sensor systems, such as seismic wave sensors, pressure sensors, temperature sensors, etc. These data have high time resolution and can reflect the immediate changes in the oil field development process. When the real-time formation measurement data is predicted using the three-dimensional underground oil field reserve model, the three-dimensional geological model, physical parameter distribution and historical exploration data established in the early stage are combined, and the oil and gas reserve assessment technology such as fluid dynamics simulation and reservoir simulation are used for real-time prediction. The oil field reserve model itself quantifies the oil field reserves in advance by means of spatial interpolation, inversion calculation and geological simulation. By combining real-time measurement data with existing models, dynamic updates and corrections are performed to predict the potential production capacity, oil and gas reservoir exploitation potential, development effects, etc. of oil fields at different development stages. Specific technical means include numerical simulation methods (such as finite difference method, finite element method) and fluid dynamics simulation, which are used to predict the oil production rate, oil and gas reserves and development effects of oil fields in the time dimension. The development potential prediction results are based on the fusion of formation measurement data and oil field reserve models, and use uncertainty analysis techniques such as multi-point geological simulation and Monte Carlo simulation to conduct sensitivity analysis and uncertainty assessment under different development scenarios. Monte Carlo simulation can quantify the uncertainty in the prediction results, evaluate the development potential range of the oil field and the risk of the development plan, and then generate a development potential report. This report will include the development potential analysis of the oil field, resource assessment, and benefit prediction of different development plans, providing decision makers with a quantitative development reference.

[0033] Step S4: measure underground oilfield exploration parameters according to the oilfield development potential report to generate oilfield exploration parameter data; update the geophysical exploration data set based on the oilfield exploration parameter data, thereby completing the quantitative operation of oilfield development potential resources.

[0034] In the embodiment of the present invention, the generation of the oil field development potential report includes a detailed analysis of the development potential of the oil field, the distribution of oil and gas reservoirs, reserve assessment and its development effect. Based on these analysis results, the development potential report provides key information about the underground oil field exploration parameters, including the physical parameters of the rock, the spatial distribution of the oil and gas reservoirs, the permeability, porosity, temperature and pressure of the reservoir. For the measurement of the exploration parameters, a series of advanced geological exploration technologies are relied on, such as downhole pressure sensors, temperature sensors, core collection and testing, porosity and permeability measurement, etc. Through core experiments and mercury injection porosity tests, physical property data of different layers of the oil field can be obtained; by collecting real-time production data of the oil field, such as oil-gas-water ratio, flow rate, water injection volume, etc., the exploration parameters can be further supplemented. Using non-invasive technologies such as acoustic time difference method, resistivity method, seismic wave method, etc., the dynamic changes of underground oil and gas reservoirs are monitored and estimated in real time, thereby providing accurate data support for subsequent resource quantitative operations. After obtaining these exploration parameter data, they need to be used to update the geophysical exploration data set. Specifically, geophysical exploration data sets include reservoir models based on seismic reflection, downhole resistivity, seismic wave velocity, amplitude and other data. By integrating oilfield exploration parameter data with these existing data, the spatial distribution, reserve assessment, permeability, porosity, lithology changes, etc. of underground oilfields can be dynamically updated. This process involves comparing and integrating exploration parameter data with historical seismic data, drilling data and other geophysical data, and using interpolation, inversion, correction and other technical means to optimize the accuracy and completeness of the data set. For example, the measured exploration parameters are spatially interpolated with the original geological data using the Kriging interpolation method or the inverse modeling method, and then the local errors of the original data are corrected to obtain an updated geophysical exploration data set.

[0035] Preferably, step S1 comprises the following steps: Step S11: Acquire a geophysical exploration data set, wherein the geophysical exploration data set includes stratigraphic data, rock property data and geophysical exploration data; Step S12: removing outliers from the formation data to generate formation data removal data; performing porosity temperature correction on the rock property data to generate rock porosity correction data; performing permeability pressure adjustment on the rock porosity correction data to generate rock property processing data; Step S13: performing medium space registration on the geophysical exploration data to generate geogeological processing data; Step S14: Perform spatial resampling standard format consistency processing on the stratigraphic data removal data, rock porosity correction data, rock property processing data and earth geology processing data to generate a geophysical exploration standard data set.

[0036] In an embodiment of the present invention, stratigraphic data, rock property data, and geophysical exploration data are extracted from multi-source geophysical exploration data. At this stage, the stratigraphic data includes basic data such as depth, rock thickness, and lithology during the drilling process; the rock property data includes physical properties such as rock porosity, permeability, and density; and the geophysical exploration data includes underground medium information obtained by seismic waves, georesistivity, or other methods, such as reflection wave amplitude, wave velocity, and waveform. These data are the basis for subsequent geological analysis and oil and gas resource assessment. The stratigraphic data is processed to remove outliers, which is part of data cleaning. The purpose is to eliminate sample data with measurement errors or abnormalities to ensure that the data set used can represent the actual situation underground. Common technical means include using statistical analysis methods, such as z-score detection method, box plot method, or distribution deviation method to identify and remove outliers. Next, the rock property data needs to be corrected for porosity temperature. This process adjusts the porosity data through the temperature correction formula to eliminate the influence of temperature on the porosity value; then, based on the relationship between permeability and pressure, the rock porosity correction data is adjusted for permeability pressure. This process often corrects the permeability change under pressure conditions through fitting curves, regression analysis or experimental data to improve the accuracy of rock property data. Geophysical exploration data medium spatial registration is to uniformly process and align geophysical exploration data from different sources or at different times so that different data have a consistent spatial coordinate system. This process involves the spatial coordinate alignment of seismic waves, georesistivity data, etc., which is achieved through image registration technology, grid conversion methods, rigid / non-rigid transformation algorithms, etc., in order to ensure the precise spatial overlap of various data and avoid spatial errors caused by differences in data sources. Stratigraphic data removal data, rock porosity correction data, rock property processing data and geogeological processing data need to be processed for spatial resampling standard format consistency. The core of this processing step is to unify the measurement units, data formats and spatial resolutions of different data sources, and use resampling technology to convert the data format and perform spatial interpolation. Commonly used resampling methods include nearest neighbor interpolation, bilinear interpolation, cubic interpolation, etc. The goal is to enable all data to be processed in a unified standard format to form a standard data set for geophysical exploration, thereby providing reliable input data for subsequent three-dimensional modeling, reserve assessment and other work.

[0037] Preferably, step S13 includes the following steps: Step S131: using seismic exploration technology to obtain geological structure reflection wave data from stratum data to generate underground geological reflection wave data; Step S132: performing high-frequency noise processing on the underground geological reflection wave data to generate underground geological noise reduction data; using tomography technology to perform wave propagation time and space error correction on the underground geological noise reduction data to generate underground geological reflection wave processing data; Step S133: acquiring resistivity information of different layers from the stratum data to generate underground layer resistivity information data; eliminating the non-uniformity error of the underground layer resistivity information data to generate underground geological resistivity processing data; Step S134: performing medium space registration on the underground geological reflection wave processing data and the underground geological resistivity processing data to generate earth geological processing data.

[0038] In the embodiment of the present invention, seismic exploration technology is used to extract geological structure reflection wave data from stratum data. Through the propagation characteristics of seismic waves, seismic exploration technology can effectively reflect the characteristics of different underground geological layers, especially the reflection of seismic waves by different stratum interfaces, so as to obtain information about underground structures. This process uses reflection seismic data processing technology, including waveform picking, time delay estimation and other methods, so as to obtain underground geological reflection wave data. High-frequency noise processing is performed on underground geological reflection wave data. Noise processing is a key step in seismic data processing, and commonly used methods include filtering, denoising and data smoothing. Through high-frequency noise filtering, interference signals caused by environmental factors, instrument errors, etc. can be effectively removed, the clarity of the signal can be enhanced, and the quality of geological reflection wave data can be improved. In addition, tomography technology is used to correct the spatiotemporal errors in the wave propagation process. This technology can correct the offset in the wave propagation through reverse propagation calculation, optimize the reflection wave data, and obtain more accurate underground geological reflection wave processing data. Use georesistivity detection means to obtain resistivity information of different layers of stratum data. The georesistivity detection technology evaluates the conductivity characteristics of each layer of soil or rock in the underground by measuring current and voltage. The resistivity difference of different strata provides important information for geological research, especially in judging underground oil and gas layers, aquifers, etc. In this process, the resistivity data can effectively identify the physical characteristics of the underground and infer the distribution of rock strata. However, due to the heterogeneity error in the strata, the subsequent processing eliminates the error of the resistivity information. This process uses data correction methods, such as error correction based on statistical analysis and interpolation methods, to reduce the errors caused by equipment heterogeneity or environmental interference, and ensure the accuracy of geological resistivity processing data. The alignment and fusion of different data sources are achieved by performing medium space registration on underground geological reflection wave processing data and underground geological resistivity processing data. Seismic reflection wave data and resistivity data describe different geological features of the underground respectively. In practical applications, they have different spatial resolutions and sampling characteristics. The medium space registration technology ensures that various types of data can be compared and comprehensively analyzed in the same spatial framework by performing coordinate system transformation, interpolation alignment and other processing on the two data. Common registration methods include rigid and non-rigid transformation based on image registration, and spatial alignment technology based on point cloud matching, which can accurately merge different types of geological data and generate more reliable geogeological processing data.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Performing oil and gas reservoir sequence stratigraphic analysis on underground geological reflection wave processing data, and obtaining oil and gas reservoir characteristic data of source oil fields; Step S22: performing seismic inversion on the underground oilfield oil and gas reservoir prediction data to generate underground oilfield oil and gas reservoir data; Step S23: Perform three-dimensional modeling using underground oil field oil and gas reservoir data and geophysical exploration standard data sets to generate a three-dimensional underground oil field reserve model.

[0040] In the embodiment of the present invention, the underground geological reflection wave processing data is analyzed in detail through the oil and gas reservoir sequence stratigraphic analysis. Sequence stratigraphic analysis is a comprehensive analysis method based on seismic data and geological data. It can identify different stratigraphic units and oil and gas reservoir layers by studying the temporal distribution and mutual relationship of the strata. The analysis uses the time and amplitude characteristics of the reflection wave to determine the distribution range and spatial position of the oil and gas reservoir through tomography technology and corresponding geological interpretation methods. During the data analysis process, the location of the potential oil and gas reservoir can be accurately predicted through the time-depth conversion, stratigraphic stratification and comparative analysis of the strata, thereby generating the oil and gas reservoir prediction data of the underground oil field. The underground oil field oil and gas reservoir prediction data is further obtained through seismic inversion technology to obtain more detailed oil and gas reservoir information. Seismic inversion technology is a process of using known seismic data to reverse the underground geological characteristics and physical parameters through numerical methods. Inversion technology can convert the reflection wave signal into physical parameters of the underground rock formation, such as elastic modulus, density and porosity, so as to reveal the spatial structure, lithological characteristics and oil and gas distribution of the oil and gas reservoir. During the inversion process, optimization techniques such as the least squares method, genetic algorithm, or Monte Carlo method are used to accurately obtain the physical properties of underground oil and gas reservoirs through inversion calculations and multiple iterations, and generate underground oil field oil and gas reservoir data. These data can not only provide a basis for reserve assessment, but also provide support for subsequent oil and gas field development and management. The oil field reserve model is established by combining underground oil field oil and gas reservoir data and geophysical exploration standard data sets through 3D modeling technology. 3D modeling technology digitally expresses information such as the spatial distribution, lithology changes, and reserve characteristics of oil and gas reservoirs through high-precision gridding. This process realizes the visualization modeling of the 3D space of oil and gas reservoirs through interpolation, gridding, and spatial reconstruction of oil and gas reservoir data. 3D modeling uses finite element method, 3D inversion method, or discretization technology to describe the spatial characteristics of underground reservoirs. The model can reflect complex geological conditions such as physical properties, bedding, and fracture distribution of oil and gas reservoirs. Through 3D modeling, not only can an accurate 3D underground oil field reserve model be generated, but also a basis can be provided for the development planning, exploration and development, and management of oil and gas fields to ensure the rationality and feasibility of the development plan.

[0041] Preferably, step S23 includes the following steps: Step S231: constructing a primary model position model using underground oilfield oil and gas reservoir data and underground geological resistivity processing data to generate an underground oilfield primary position model; Step S232: using the formation data to remove data, the physical property parameters of the primary location model of the underground oil field are quantitatively processed to generate a first-layer model of physical property quantification; Step S233: using the rock porosity correction data and the rock property processing data to qualitatively characterize the acoustic wave parameters of the lithology storage layer of the primary location model of the underground oil field, and generating a lithology quantitative second layer model; Step S234: spatially discretize the first-layer quantitative physical property model and the second-layer quantitative lithology model to grid, and generate an underground oil field grid model; perform multi-point geological simulation on the underground oil field grid model to generate an underground oil field multi-point simulation model; perform reserve interpolation on the underground oil field multi-point simulation model to generate a three-dimensional underground oil field reserve model.

[0042] In the embodiment of the present invention, the primary model position model is constructed by processing the underground oil field oil and gas reservoir data and the underground geological resistivity data. In this process, the underground geological data and resistivity data are combined, and the data of different levels and scales are integrated by numerical simulation and inversion methods, and the preliminary spatial position model of the underground oil field is constructed. This process uses finite difference method, finite element method or similarity interpolation technology to integrate different measurement data into a unified spatial model while considering the geological structure, so as to provide basic data for subsequent oil field reserve assessment. The physical property parameters of the primary position model of the underground oil field are quantitatively processed by removing the data of the formation data. The key to this process is to reduce noise interference and improve the accuracy and reliability of the data by cleaning the formation data and removing outliers. Through this method, the primary position model is refined by combining physical property parameters such as porosity and permeability to generate a first-layer model of physical property quantification. The quantitative processing of physical property parameters accurately calculates and quantifies the physical properties in the model through regression analysis, machine learning algorithm or multiple regression method. The rock porosity correction data and rock property processing data are used to qualitatively characterize the acoustic parameters of the lithologic storage layer of the primary location model of the underground oil field, and generate a second-layer lithologic quantitative model. This process mainly involves rock porosity correction, lithologic data correction, and lithologic comparative analysis, and the lithologic qualitative characterization is performed through statistical methods and empirical formulas, thereby generating a lithologic model with high accuracy. Based on the first-layer physical property quantitative model and the second-layer lithologic quantitative model, the underground oil field grid model is generated through spatial discrete gridding technology. This process discretizes the continuous underground structure data into grid data, which is convenient for subsequent numerical simulation and analysis. After gridding, multi-point geological simulation technology (such as multi-point statistical simulation and sequential assignment simulation) is used to perform multi-point simulation of the underground oil field, taking into account spatial heterogeneity and uncertainty, to generate a more accurate oil field model. Finally, the reserve interpolation method is used to calculate the reserves of the multi-point simulation model to generate a three-dimensional underground oil field reserve model. The reserve interpolation process often uses Kriging interpolation or inverse distance weighted method to estimate the oil field reserves based on the changes in physical parameters in different regions.

[0043] Preferably, step S232 includes the following steps: Step S2321: using formation data to remove data, perform core experiments to generate formation core data; perform mercury injection porosity measurement on the formation core data to generate formation porosity physical property parameter data; Step S2322: performing reflection amplitude correlation analysis on the formation porosity physical parameter data and the underground geological reflection wave processing data, and constructing a porosity spatial distribution layer to generate an oilfield porosity spatial distribution layer; Step S2323: Calculate the oil and gas mobility of the formation data removal data to generate preliminary data of underground oil field permeability; perform core permeability test on the preliminary data of underground oil field permeability to generate underground oil field permeability test data; construct a permeability distribution layer according to the underground oil field oil and gas reservoir data and the underground oil field permeability test data to generate an underground oil field permeability distribution layer; Step S2324: perform physical property parameter spatial modeling on the primary location model of the underground oil field using the oil field porosity spatial distribution layer and the underground oil field permeability distribution layer to generate a first-layer model of physical property quantitativeness.

[0044] In an embodiment of the present invention, a core experiment is performed by removing data from the formation data to obtain formation core data. This process includes extracting core samples from the drilling process and conducting physical experiments to determine the physical properties of the core, especially key parameters such as porosity and permeability. Subsequently, the formation core data is subjected to mercury injection porosity measurement, which is to measure the porosity of the rock by a high-pressure mercury penetration method, thereby generating formation porosity physical property parameter data, which provides basic physical properties for subsequent modeling. Reflection amplitude correlation analysis is performed in combination with the formation porosity physical property parameter data and underground geological reflection wave processing data. This analysis process reveals the spatial distribution characteristics of formation porosity by performing correlation analysis on the amplitude data of the reflection wave. Subsequently, according to the analysis results, a porosity spatial distribution layer is constructed to generate an oil field porosity spatial distribution layer, which can reflect the porosity differences in different areas of the oil field and provide accurate spatial data support for further reservoir evaluation. The purpose of calculating the fluidity of oil and gas by removing the data from the formation data is to analyze the fluidity of oil and gas in the formation, and to calculate the preliminary permeability data of the underground oil field through different fluidity models (such as Darcy's law or fluid dynamics model). These preliminary data are further tested by core permeability test to obtain the underground oil field permeability test data, which is measured by physical experiments and can provide more accurate permeability parameters. On this basis, the permeability distribution layer is constructed by combining the oil and gas reservoir data of the underground oil field with the permeability test data, and the underground oil field permeability distribution layer is generated. This layer is used to characterize the spatial distribution of permeability in the underground oil field and provide data support for subsequent oil field development and resource evaluation. The spatial distribution layer of oil field porosity and the permeability distribution layer of underground oil fields are used to perform spatial modeling of physical property parameters on the primary location model of the underground oil field. This process is to introduce the data of the spatial distribution layer into the preliminary location model, and combine the physical property parameters such as porosity and permeability with the spatial location through numerical modeling technology to generate the first-layer model of physical property quantitative.

[0045] Preferably, step S233 includes the following steps: Step S2331: performing core analysis on the rock property processing data to generate underground oil field core analysis data; Step S2332: Perform storage layer regression correction on the underground oil field core analysis data using the rock porosity correction data to generate underground oil field storage regression data; perform acoustic wave time difference anomaly detection on the underground oil field storage regression data, and remove outliers to generate underground oil field storage layer acoustic wave data; Step S2333: Use underground geological resistivity processing data to perform core resistivity testing on underground oil field core analysis data, and combine underground oil field storage layer acoustic wave data to qualitatively characterize the lithology storage layer acoustic wave parameters of the underground oil field primary location model to generate a lithology quantitative second layer model.

[0046] In the embodiment of the present invention, the rock property processing data is analyzed in detail by core analysis technology. Core analysis is a key link in underground oil field exploration. It obtains key data such as porosity, permeability, mineral composition, lithology, etc. by measuring the physical properties of core samples taken from underground. The data generates the core analysis data of the underground oil field, which provides detailed rock formation information for subsequent physical property modeling and oil field resource evaluation. The rock porosity correction data is used to perform storage layer regression correction on the underground oil field core analysis data. The regression correction technology models the relationship between the known core data and the actual geological conditions, and derives the correction formula for the core data, thereby obtaining more accurate storage layer regression data. In addition, the underground oil field storage regression data must also undergo acoustic wave time difference anomaly detection, and by analyzing the propagation time difference of the acoustic wave data in different reservoirs, the outliers in the measurement process are identified and eliminated. This outlier elimination process can be carried out by statistical methods such as median filtering, chi-square test, etc., to eliminate experimental errors or data deviations and generate accurate underground oil field storage layer acoustic wave data. As an important indicator of rock porosity and permeability, acoustic time difference can more accurately reflect the actual physical state of underground reservoirs after processing. Combined with underground geological resistivity processing data, core resistivity test is performed on underground oil field core analysis data. Core resistivity test is to measure the resistance value through the current penetrating the core sample, so as to infer the properties of rock formation such as porosity, permeability and water saturation. After combining this test data with the acoustic wave data of the storage layer, the qualitative processing of the acoustic wave parameters of the lithology storage layer of the primary location model of the underground oil field is further enhanced. Qualitative processing refers to the generation of a quantitative second-layer model of lithology by giving different lithology categories to the lithology model by integrating the attributes of different data sources (such as resistivity, acoustic wave velocity, etc.).

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating prediction results for oil field development potential; Step S32: performing spatial heterogeneity analysis on the oilfield development potential prediction results to generate spatial uncertainty analysis data for underground oilfields; Step S33: Generate an oilfield development potential report based on the spatial uncertainty analysis data of the underground oilfield.

[0048] In the embodiment of the present invention, by obtaining the real-time measurement data of the formation, it mainly refers to collecting the real-time exploration data in the formation, such as physical parameter data such as temperature, pressure, flow, porosity, permeability, etc. These data are collected in real time by sensors and measuring equipment, and can be stored and processed in the oil field monitoring system. By combining these real-time measurement data with the three-dimensional underground oil field reserve model, predictive analysis of the oil field can be performed. The core technical means of this process is to predict the development potential of the oil field in the future based on the relationship between the reserve model and the real-time data through numerical simulation and regression analysis methods. Through these technical means, the development potential prediction results of the oil field can be output to provide a decision-making basis for subsequent resource development. Spatial heterogeneity analysis is performed on the prediction results of the development potential of the oil field. Spatial heterogeneity analysis is a geological modeling technology that aims to identify the differences in physical properties between different spatial locations, such as the variation patterns of the porosity, permeability and other reservoir characteristics of the rock formation in different geological blocks. The analysis is conducted through spatial statistical methods such as Kriging interpolation and spline interpolation, which can effectively deal with the non-uniformity and randomness of spatial data, and reveal the distribution and variability of oilfield resources by modeling spatial data. Through these analysis methods, spatial uncertainty analysis data of underground oilfields are generated, which can not only reflect the spatial distribution of oilfield reserves, but also provide a quantitative basis for further resource assessment. Based on the aforementioned spatial uncertainty analysis data, a development potential report is generated. Specific technical means include using risk analysis methods (such as Monte Carlo simulation) to conduct uncertainty analysis on the potential assessment results of underground oilfields, so as to obtain an accurate development potential assessment report.

[0049] Preferably, step S32 includes the following steps: Step S321: Perform lithology and porosity analysis on the oilfield development potential prediction results to generate oilfield location attribute analysis data; perform multi-point simulation on the oilfield location attribute analysis data to generate oilfield location attribute simulation data; Step S322: Eliminate lithologic heterogeneity from the oilfield location attribute simulation data to generate oilfield location prediction elimination data; perform exploration data error analysis based on the oilfield location prediction elimination data to generate oilfield exploration location data; Step S323: Perform spatial heterogeneity analysis on the oilfield exploration location data to generate underground oilfield spatial uncertainty analysis data.

[0050] In the embodiment of the present invention, the prediction result of oilfield development potential needs to be analyzed for lithology and porosity. The key technical means of this process is to extract, screen and analyze the lithology attributes and porosity data of the oilfield development potential prediction data to determine the physical property characteristics of different locations of the oilfield. These data involve information on multiple levels such as the porosity, permeability, pore structure and lithology changes of rocks. By establishing mathematical models of geology and petrology, the physical properties of various regions of the oilfield can be accurately obtained. The generation of these data can rely on experimental data such as formation analysis and core testing, and can be supplemented by data fitting techniques such as the least squares method to quantify the porosity and lithology parameters. Next, these attribute data are used to perform multi-point simulation to generate oilfield location attribute simulation data. Multi-point simulation is based on geostatistical methods (such as sequential simulation, collaborative simulation, etc.), combined with data from different points in the oilfield, to simulate the spatial distribution of lithology and porosity in the entire region. This technology can more accurately reflect the changes in the physical properties of underground oilfields and generate a global oilfield location attribute model. For the oilfield location attribute simulation data, lithology heterogeneity is eliminated. The key to this technical means is to use spatial statistical methods and filtering algorithms to identify and remove the inhomogeneous parts in the oilfield location data, especially in areas with significant fluctuations or errors in spatial distribution. Through this step, the reliability and accuracy of the oilfield prediction results can be improved, ensuring that the data in the model is smoother and more consistent. In addition, combined with the exploration data error analysis technology, the oilfield location prediction exclusion data is further corrected to generate oilfield exploration location data. This process involves error evaluation of known exploration data, using error propagation method, sensitivity analysis and other methods to correct the data, eliminate the uncertainty caused by measurement error, equipment deviation, etc., so as to obtain more accurate oilfield location data. The spatial heterogeneity of oilfield exploration location data is analyzed by using Monte Carlo simulation. Monte Carlo simulation is a numerical simulation method based on random sampling and probability distribution, which is widely used in geological exploration to evaluate the uncertainty in oilfield development. Through multiple random simulations of exploration location data, oilfield development prediction results under multiple different scenarios can be generated, thereby evaluating the impact of spatial heterogeneity on oilfield development potential.

[0051] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: measuring underground oil field survey parameters according to the oil field development potential report to generate oil field survey parameter data; Step S42: abnormal fluctuation detection is performed on the oilfield survey parameter data, and abnormal fluctuation elimination is performed to generate oilfield survey selection data; Step S43: updating the geophysical exploration data set based on the oilfield survey and selection data, thereby completing the quantitative operation of oilfield development potential resources.

[0052] In the embodiment of the present invention, the process of measuring underground oil field survey parameters according to the oil field development potential report mainly relies on the combination of oil field exploration data acquisition and physical measurement methods. Oil field survey parameters include multiple physical property data such as pressure, temperature, fluid properties, permeability, etc. of underground oil reservoirs. These parameters are collected in real time by technical means such as downhole equipment and sensors. Common survey parameter measurement means include wellhead pressure sensors, temperature sensors, fluid analysis instruments, etc. By recording these sensor data in real time, the dynamic characteristics of the oil field can be accurately reflected, and oil field survey parameter data can be generated. At this time, the accuracy and frequency of data acquisition directly affect the accuracy of subsequent analysis and decision-making. Therefore, the calibration of sensors and the stability of data acquisition are required to be high. Abnormal fluctuation detection and elimination are performed on oil field survey parameter data. This process uses time series analysis methods or filtering algorithms to identify and remove abnormal fluctuations in data. For example, based on the standard deviation or rate of change of data, a threshold model can be designed to automatically detect sudden fluctuations in data and exclude abnormal values ​​caused by measurement equipment errors, environmental interference, and other factors. In addition, commonly used anomaly detection methods such as sliding window-based algorithms, Kalman filtering, and wavelet transforms can effectively remove noise data and improve data reliability. After the abnormal fluctuations are removed, the generated "oil field exploration and selection data" will more accurately reflect the actual situation of the oil field and provide higher quality data support for subsequent analysis. It involves using "oil field exploration and selection data" to update the geophysical exploration data set, mainly to correct and optimize the geophysical exploration data (such as seismic data, resistivity data, acoustic wave data, etc.) collected in the early stage. This process uses data fusion technology to combine oil field exploration parameters with existing geophysical data, and updates the geophysical exploration data set through weighted averaging, data interpolation, least squares method and other methods.

[0053] The beneficial effect of the present invention is that, by obtaining geophysical exploration data and preprocessing, the quality of input data is ensured, noise and inconsistency are eliminated, thereby providing a reliable basis for the subsequent construction of a three-dimensional geological model. By generating a standardized data set, not only the uniformity of the data is ensured, but also data support is provided for further spatial distribution modeling. In step S2, a three-dimensional geological model is constructed based on a geophysical exploration standard data set, providing an accurate geological background for the estimation of underground oil field reserves. The model realizes a refined assessment of reserves by integrating physical parameters such as porosity, permeability, and lithology. Step S3 predicts the real-time measurement data using the generated reserve model, updates the prediction results of oil field development potential in combination with real-time data, and provides a scientific basis for development decisions. By generating an oil field development potential report, data support is further provided for the optimization of the development process. Step S4 measures the oil field exploration parameters according to the development potential report, and updates the geophysical exploration data set, so that the oil field development potential resource assessment continues to track the actual development situation, and can adjust the assessment model and development strategy in time. This full-process data integration and optimization mechanism effectively improves the accuracy and real-time performance of oilfield resource assessment, and provides reliable data support for the sustainable development of oilfields. Therefore, the present invention solves the problem of lack of real-time performance and accuracy in traditional oilfield assessment by realizing dynamic monitoring and updating of oilfield development potential, and improves the resource utilization efficiency of the oilfield development process.

[0054] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0055] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A quantitative evaluation method for the potential resources of oil field development, characterized in that: The following steps are involved: Step S1: obtaining a geophysical exploration data set; performing data preprocessing on the geophysical exploration data set to generate a geophysical exploration standard data set; Step S2: constructing a three-dimensional geological model based on a geophysical exploration standard data set to generate a three-dimensional underground oil field reserve model; Step S3: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating prediction results for oil field development potential; Generate a development potential report based on the oil field development potential prediction results; Step S4: measuring underground oil field exploration parameters according to the oil field development potential report to generate oil field exploration parameter data; The geophysical exploration data set is updated based on the oil field exploration parameter data to complete the quantitative operation of oil field development potential resources.

2. The quantitative evaluation method of oil field development potential resources according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a geophysical exploration data set, wherein the geophysical exploration data set includes stratigraphic data, rock property data and geophysical exploration data; Step S12: removing outliers from the formation data to generate formation data removal data; performing porosity temperature correction on the rock property data to generate rock porosity correction data; performing permeability pressure adjustment on the rock porosity correction data to generate rock property processing data; Step S13: performing medium space registration on the geophysical exploration data to generate geogeological processing data; Step S14: Perform spatial resampling standard format consistency processing on the stratigraphic data removal data, rock porosity correction data, rock property processing data and earth geology processing data to generate a geophysical exploration standard data set.

3. The quantitative evaluation method of oil field development potential resources according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: using seismic exploration technology to obtain geological structure reflection wave data from stratum data to generate underground geological reflection wave data; Step S132: performing high-frequency noise processing on the underground geological reflection wave data to generate underground geological noise reduction data; using tomography technology to perform wave propagation time and space error correction on the underground geological noise reduction data to generate underground geological reflection wave processing data; Step S133: acquiring resistivity information of different layers from the stratum data to generate underground layer resistivity information data; eliminating the non-uniformity error of the underground layer resistivity information data to generate underground geological resistivity processing data; Step S134: performing medium space registration on the underground geological reflection wave processing data and the underground geological resistivity processing data to generate earth geological processing data.

4. The quantitative evaluation method of oil field development potential resources according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Performing oil and gas reservoir sequence stratigraphic analysis on underground geological reflection wave processing data, and obtaining oil and gas reservoir characteristic data of source oil fields; Step S22: performing seismic inversion on the underground oilfield oil and gas reservoir prediction data to generate underground oilfield oil and gas reservoir data; Step S23: Perform three-dimensional modeling using underground oil field oil and gas reservoir data and geophysical exploration standard data sets to generate a three-dimensional underground oil field reserve model.

5. The quantitative evaluation method of oil field development potential resources according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: constructing a primary model position model using underground oilfield oil and gas reservoir data and underground geological resistivity processing data to generate an underground oilfield primary position model; Step S232: using the formation data to remove data, the physical property parameters of the primary location model of the underground oil field are quantitatively processed to generate a first-layer model of physical property quantification; Step S233: using the rock porosity correction data and the rock property processing data to qualitatively characterize the acoustic wave parameters of the lithology storage layer of the primary location model of the underground oil field, and generating a lithology quantitative second layer model; Step S234: spatially discretize the first-layer quantitative physical property model and the second-layer quantitative lithology model to grid, and generate an underground oil field grid model; perform multi-point geological simulation on the underground oil field grid model to generate an underground oil field multi-point simulation model; perform reserve interpolation on the underground oil field multi-point simulation model to generate a three-dimensional underground oil field reserve model.

6. The quantitative evaluation method of oil field development potential resources according to claim 5, characterized in that: Step S232 includes the following steps: Step S2321: using formation data to remove data, perform core experiments to generate formation core data; perform mercury injection porosity measurement on the formation core data to generate formation porosity physical property parameter data; Step S2322: performing reflection amplitude correlation analysis on the formation porosity physical parameter data and the underground geological reflection wave processing data, and constructing a porosity spatial distribution layer to generate an oilfield porosity spatial distribution layer; Step S2323: Calculate the oil and gas mobility of the formation data removal data to generate preliminary data of underground oil field permeability; perform core permeability test on the preliminary data of underground oil field permeability to generate underground oil field permeability test data; construct a permeability distribution layer according to the underground oil field oil and gas reservoir data and the underground oil field permeability test data to generate an underground oil field permeability distribution layer; Step S2324: perform physical property parameter spatial modeling on the primary location model of the underground oil field using the oil field porosity spatial distribution layer and the underground oil field permeability distribution layer to generate a first-layer model of physical property quantitativeness.

7. The quantitative evaluation method of oil field development potential resources according to claim 5, characterized in that: Step S233 includes the following steps: Step S2331: performing core analysis on the rock property processing data to generate underground oil field core analysis data; Step S2332: Perform storage layer regression correction on the underground oil field core analysis data using the rock porosity correction data to generate underground oil field storage regression data; perform acoustic wave time difference anomaly detection on the underground oil field storage regression data, and remove outliers to generate underground oil field storage layer acoustic wave data; Step S2333: Use underground geological resistivity processing data to perform core resistivity testing on underground oil field core analysis data, and combine underground oil field storage layer acoustic wave data to qualitatively characterize the lithology storage layer acoustic wave parameters of the underground oil field primary location model to generate a lithology quantitative second layer model.

8. The quantitative evaluation method of oil field development potential resources according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: acquiring real-time formation measurement data; using a three-dimensional underground oil field reserve model to predict underground oil fields based on the real-time formation measurement data, and generating prediction results for oil field development potential; Step S32: performing spatial heterogeneity analysis on the oilfield development potential prediction results to generate spatial uncertainty analysis data for underground oilfields; Step S33: Generate an oilfield development potential report based on the spatial uncertainty analysis data of the underground oilfield.

9. The quantitative evaluation method of oil field development potential resources according to claim 8, characterized in that: Step S32 includes the following steps: Step S321: Perform lithology and porosity analysis on the oilfield development potential prediction results to generate oilfield location attribute analysis data; perform multi-point simulation on the oilfield location attribute analysis data to generate oilfield location attribute simulation data; Step S322: Eliminate lithologic heterogeneity from the oilfield location attribute simulation data to generate oilfield location prediction elimination data; perform exploration data error analysis based on the oilfield location prediction elimination data to generate oilfield exploration location data; Step S323: Perform spatial heterogeneity analysis on the oilfield exploration location data to generate underground oilfield spatial uncertainty analysis data.

10. The quantitative evaluation method of oil field development potential resources according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: measuring underground oil field survey parameters according to the oil field development potential report to generate oil field survey parameter data; Step S42: abnormal fluctuation detection is performed on the oilfield survey parameter data, and abnormal fluctuation elimination is performed to generate oilfield survey selection data; Step S43: updating the geophysical exploration data set based on the oilfield survey and selection data, thereby completing the quantitative operation of oilfield development potential resources.

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