Exploration and development data fusion method and system

Abstract

The invention relates to the technical field of geological exploration data processing, in particular to an exploration and development data fusion method and system. The method comprises the following steps: S1, collecting historical exploration and development data of a target area in a non-rainfall climate; s2, constructing a climate influence evaluation model; s3, performing accuracy evaluation on the exploration and development data of the target area under the rainwater climate by using the climate influence evaluation model; s4, establishing an error verification evaluation model, and verifying an evaluation result of the climate influence evaluation model; s5, forming reserved data; s6, adjusting the exploration scheme through the reserved data; s7, performing exploration development according to the adjusted exploration scheme; according to the method, the cost required by data collection for judging the influence degree of the exploration data by the rainwater is reduced by deducing the past data, and the deducing result is compared with the actual situation for verification by deducing various information changes possibly caused by the rainwater climate, so that the exploration accuracy and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration data processing, and particularly to a method and system for fusing exploration and development data. Background Art

[0002] In the process of exploring oil and gas resources in desert areas, it is necessary to first understand the local geological data, which plays an important role in grasping the underground geological structure and resource distribution of the exploration area. However, when rain weather occurs during data exploration, due to climate changes and phenomena such as rising groundwater caused by rain weather, not only may the geological conditions change, but also the accuracy of exploration equipment may decrease due to changes in air humidity. Some of the data measured at this time may be inaccurate or data that does not conform to the general environmental trend of the desert arid area. During the process of exploring oil and gas resources, if there are errors in data quality, it is very likely to cause resource waste and even increase exploration risks.

[0003] The prior art CN112836857A discloses an oil and gas exploration optimization method and device. The method includes: determining the data interval means of drilling historical data and seismic historical data within a set time range; the data interval means include the data interval means of drilling seismic exploration project costs and the data interval means of drilling seismic exploration project effectiveness; calculating the recoverable reserves according to the expected target of oil and gas exploration reserves; determining the discovery cost according to the international crude oil price; the discovery cost includes the oil discovery cost, the natural gas discovery cost, and the oil and gas discovery cost; calculating the drilling capabilities of different oil and gas fields according to the drilling historical data; inputting the data interval means, recoverable reserves, discovery cost, and drilling capabilities into a pre-established plan optimization model for oil and gas exploration optimization. However, the above method does not consider the impact of rain weather on oil and gas resource exploration in desert areas, and does not optimize the inaccurate data affected by rain or the data that does not conform to the general environmental trend of the desert arid area, resulting in inaccurate oil and gas resource exploration.

[0004] Therefore, there is an urgent need to provide a method and system for fusing exploration and development data. Summary of the Invention

[0005] The present invention solves the technical problems existing in the prior art, and provides a method and system for fusing exploration and development data.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] An exploration and development data fusion method includes the following steps:

[0008] S1. Collect historical exploration and development data under non-rainy climate in the target area;

[0009] S2. Construct a climate impact assessment model based on the historical exploration and development data collected in step S1;

[0010] S3. Obtain the exploration and development data under the rain climate of the target area, use the climate impact assessment model obtained in step S2 to evaluate the accuracy of the exploration and development data under the rain climate, and determine whether to give corrective suggestions according to the evaluation results;

[0011] S4. Establish an error verification assessment model to verify the evaluation results of the climate impact assessment model in step S3;

[0012] S5. Retain the part where the evaluation results of the climate impact assessment model are the same as the verification results of the error verification assessment model to form retained data;

[0013] S6. Obtain the exploration plan, adjust the exploration plan through the retained data, re-detect the un-retained part of the data, and add it to the exploration plan;

[0014] S7. Carry out exploration and development according to the adjusted exploration plan, monitor the exploration and development process in real time, and feed it back to the database.

[0015] Further, S2 specifically includes the following steps:

[0016] S201. Summarize and integrate the historical exploration and development data under the non-rainy climate, and perform standardization processing;

[0017] S202. Use the standardized historical exploration and development data to construct a climate impact assessment model to obtain the theoretical values of the occurrence probabilities of various exploration and development data under the rain climate.

[0018] Furthermore, S202 specifically includes the following steps:

[0019] S2021. Obtain a set of candidate values through the Metropolis-Hastings algorithm or Gibbs;

[0020] S2022. Select one from the standardized historical exploration data as the initial value, and then combine it with the set of candidate values obtained in S2021 to obtain the values of various exploration and development data under the rain climate through the Markov chain Monte Carlo method;

[0021] S2023. Obtain the theoretical values of the occurrence probabilities of various exploration and development data under the rain climate at the stationary distribution through Bayesian statistical methods.

[0022] Further, S3 specifically includes the following steps:

[0023] S301. Calculate the coincidence degree between the values obtained from the climate impact assessment model and the exploration and development data under the rain climate of the target area obtained;

[0024] S302. Compare the coincidence degree calculated in step S301 with the preset accuracy threshold. When the coincidence degree is less than the preset accuracy threshold, it indicates that the exploration and development data under the rain climate is inaccurate, and the exploration and development data under the rain climate needs to be corrected. When the coincidence degree is greater than or equal to the preset accuracy threshold, it indicates that the exploration and development data under the rain climate is accurate, and there is no need to correct the exploration and development data under the rain climate.

[0025] Furthermore, step S301 specifically calculates the coincidence degree through the following formula:

[0026]

[0027]

[0028] In the above formula, A i represents the coincidence degree of the i-th exploration and development data under the rain climate measured in the target area, represents the value obtained from the climate impact assessment model for the i-th exploration and development data in the target area, represents the value measured for the i-th exploration and development data under the rain climate in the target area, represents the average coincidence degree of the exploration and development data under the rain climate in the target area, and n represents the types of data in the exploration and development data in the target area.

[0029] Further, S4 specifically includes the following steps:

[0030] S401. Construct an error verification and assessment model, which includes an instrument precision offset coefficient, an exploration seismic wave propagation influence coefficient, and a geochemical property change coefficient;

[0031] S402. According to the error verification and assessment model, calculate the error verification and assessment index, and evaluate the assessment result of the climate impact assessment model in step S3 according to the error verification and assessment index.

[0032] Furthermore, the instrument precision offset coefficient in S401 is calculated through the following formula:

[0033]

[0034] In the above formula, J y represents the instrument precision offset coefficient, y represents the number of different instruments, y = 1, 2, 3, 4,..., u, u is a positive integer, S y is the instrument measurement data under the rain climate, Represents the average of the detection data of different instruments under non-rainy climate conditions.

[0035] Furthermore, the exploration seismic wave propagation influence coefficient in S401 is specifically calculated by the following formula:

[0036]

[0037] In the above formula, C k Represents the exploration seismic wave propagation influence coefficient, H sw Represents the groundwater level height during exploration under rainy climate conditions, Represents the target area water level standard value obtained from the recorded annual average groundwater level height of the target area.

[0038] Furthermore, the coefficient of change in geochemical properties in S401 is obtained by the following formula:

[0039]

[0040] In the above formula, H d Represents the coefficient of change in geochemical properties, x represents the number obtained by sampling at different heights of the well logging, x = 1, 2, 3, 4,..., j, where j is a positive integer, and R x Represents the soil chemical composition measured at different heights of the well logging, and R ′ x Represents the soil chemical composition measured under daily non-rainy climate conditions corresponding to the height x obtained from historical data.

[0041] Furthermore, the error verification and evaluation index in S402 is specifically calculated by the following formula:

[0042] PG wc = e1 × J y + e2 × C k + e3 × H d

[0043] In the above formula, PG wc Represents the error verification and evaluation index, and e1, e2, and e3 are different preset proportionality coefficients of the instrument precision deviation coefficient, the exploration seismic wave propagation influence coefficient, and the coefficient of change in geochemical properties, respectively.

[0044] Furthermore, the specific method for evaluating the evaluation result of the climate impact evaluation model in S3 according to the error verification and evaluation index in S402 is as follows: Compare the error verification and evaluation index with a preset threshold. When the error verification and evaluation index is less than the preset threshold, it indicates that the evaluation result of the climate impact evaluation model is incorrect. When the error verification and evaluation index is greater than or equal to the preset threshold, it indicates that the evaluation result obtained by the climate impact evaluation model is correct.

[0045] Further, the historical exploration and development data obtained in step S2 under non-rainy climate includes seismic wave information, formation information, electromagnetic information, and drilling information.

[0046] Furthermore, the formation information includes rock samples, core analysis, and rock physical property data.

[0047] Furthermore, the drilling information includes borehole logs, wellbore profile data, wellbore temperature, and pressure data.

[0048] A system using any one of the exploration and development data fusion methods described above includes a data collection module, a climate impact assessment module, a comparison and correction module, an error verification and assessment module, a processing module, an adjustment module, and a monitoring and feedback module connected in sequence; the data collection module is used to execute the content described in step S1, the climate impact assessment module is used to execute the content described in step S2, the comparison and correction module is used to execute the content described in step S3, the error verification and assessment module is used to execute the content described in step S4, the processing module is used to execute the content described in step S5, the adjustment module is used to execute the content described in step S6, and the monitoring and feedback module is used to execute the content described in step S7.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] (1) By fusing the exploration data under non-rainy climate in the target area, the present invention constructs a climate impact assessment model, infers the situation of the exploration data under rainy climate, and compares it with the existing data based on this to determine whether the rainy climate has an impact on the exploration data. After the judgment is completed, it is further deduced and verified through the impact of rain on the exploration process. By first deducing the past data, the cost of data collection required to determine the degree of influence of exploration data by rain is reduced. Then, through the deduction of various information changes that may be caused by rainy climate, the deduced results are compared and verified with the actual situation to determine whether the exploration data is only affected by rainy climate, so as to facilitate the adjustment of subsequent geological exploration strategies, improve the accuracy and safety of exploration, and at the same time optimize the database for subsequent exploration operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flowchart of the method of the present invention.

[0052] Figure 2 is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] As Figure 1 shown, the present invention provides an exploration and development data fusion method, including the following steps:

[0055] S1. Collect historical exploration and development data under non-rainy climate in the target area.

[0056] Specifically, the historical exploration and development data under non-rainy climate includes seismic wave information, formation information, electromagnetic information, and drilling information. Among them, the seismic wave information includes seismic reflection data and seismic velocity data, and the seismic wave information is used to draw the structure of underground rock formations and determine the location of potential oil and gas reservoirs. The formation information includes rock samples, core analysis, and rock physical property data, which are used to determine rock types, rock properties, porosity, permeability, etc., so as to evaluate oil and gas reserves and reservoir characteristics. The electromagnetic information refers to electromagnetic exploration data used to detect underground conductors. The drilling information includes borehole logs, wellbore profile data, wellbore temperature, and pressure data, which are used to analyze the formation conditions in the well and the wellbore state.

[0057] The historical exploration and development data under non-rainy climate in the target area is sourced from the database established by the oilfield enterprise's historical exploration and development.

[0058] S2. Construct a climate impact assessment model based on the historical exploration and development data, specifically including the following steps:

[0059] S201. Summarize and fuse the historical exploration and development data under non-rainy climate (i.e., dry climate), and standardize the data from different data sources to achieve unified format.

[0060] S202. Use the standardized historical exploration and development data to construct a climate impact assessment model to obtain the theoretical values of the occurrence probabilities of various historical exploration and development data under the current rainy climate, specifically:

[0061] S2021. Obtain a set of candidate values through the Metropolis-Hastings algorithm or Gibbs.

[0062] S2022. Select one from the standardized historical exploration data as the initial value, and then combine it with the set of candidate values obtained in step S2021 to obtain the data values of various exploration and development under rainy climate through the Markov chain Monte Carlo method.

[0063] S2023. Obtain the theoretical values of the probabilities of various data values in exploration and development under rain climate when obtaining the stationary distribution through Bayesian statistical methods.

[0064] S3. Obtain the exploration and development data under the rain climate of the target area, use the climate impact assessment model obtained in step S2 to evaluate the accuracy of the exploration and development data under the rain climate, and determine whether to give corrective suggestions according to the evaluation results. The specific steps are as follows:

[0065] S301. Conduct an overlap analysis on the values obtained from the climate impact assessment model and the exploration and development data measured under the current rain climate. Specifically, it is calculated through the following formula:

[0066]

[0067]

[0068] In the above formula, A i represents the overlap degree of the i-th exploration and development data measured in the target area under the rain climate, represents the value obtained from the climate impact assessment model for the i-th exploration and development data in the target area, represents the value measured for the i-th exploration and development data in the target area under the rain climate, represents the average overlap degree of the exploration and development data in the target area under the rain climate, and n represents the types of data in the exploration and development data of the target area.

[0069] S302. Compare the average overlap degree of the target area obtained in step S301 with the preset accuracy threshold. Specifically: when the average overlap degree of the target area is less than the preset accuracy threshold, it indicates that the exploration and development data under the rain climate is inaccurate or not suitable for the development work under the conventional climate of the target area, and it is necessary to correct the exploration and development data under the rain climate. The corrected data is the data of the climate impact assessment model, and the data information is transmitted to the subsequent steps; when the average overlap degree of the target area is greater than or equal to the preset accuracy threshold, it indicates that the exploration and development data under the rain climate is accurate or the rain climate has little geological impact on the target area and does not have a great impact on the development work. This exploration and development data can be used and does not need to be corrected.

[0070] S4. Build an error verification and evaluation model based on the instrument impact degree under the rain climate and the measurement error amount caused by the change of the groundwater level. Use the error verification and evaluation model to verify the evaluation results of the climate impact assessment model in step S3. Specifically:

[0071] S401. The error verification and evaluation model includes an instrument precision offset coefficient, an exploration seismic wave propagation influence coefficient, and a geochemical property change coefficient. Specifically:

[0072] (1) The instrument precision offset coefficient is the information on the influence degree of the instrument under the obtained rain climate. The instrument precision offset coefficient is calculated by the following formula:

[0073]

[0074] In the above formula, J y represents the instrument precision offset coefficient, y represents the number of different instruments, y = 1, 2, 3, 4,..., u, where u is a positive integer, and S y is the instrument measurement data under the rain climate, represents the average value of the detection data of different instruments under the non-rain climate,

[0075] (2) Both the exploration seismic wave propagation influence coefficient and the geochemical property change coefficient are the measurement error amounts caused by the change of the groundwater level. Specifically:

[0076] The exploration seismic wave propagation influence coefficient is specifically obtained by the following formula:

[0077]

[0078] In the above formula, C k represents the exploration seismic wave propagation influence coefficient, H sw represents the groundwater level height during exploration under the rain climate, represents the target area water level standard value obtained from the recorded annual average groundwater level height of the target area.

[0079] Since the propagation speeds of exploration seismic waves in water and geology are different, to obtain the exploration seismic wave propagation influence coefficient, only the exploration depth range and the rise of groundwater need to be obtained. The higher the rise of groundwater, the greater the influence on the propagation of exploration seismic waves. The smaller the rise of groundwater, the smaller the influence on the propagation of exploration seismic waves.

[0080] The geochemical property change coefficient is obtained by the following formula:

[0081]

[0082] In the above formula, H d represents the geochemical property change coefficient, x represents the number obtained by sampling at different heights of the logging, x = 1, 2, 3, 4,..., j, where j is a positive integer, and R x represents the soil chemical composition measured at different heights of the logging, and R ′ xIt represents the soil chemical components measured under the daily non-rainy climate corresponding to the height x obtained from historical data.

[0083] S402. According to the error verification evaluation model, calculate the error verification evaluation index; when the error verification evaluation index is less than the preset threshold, it indicates that the rain climate has little impact on the exploration, indicating that the climate impact evaluation model is inaccurate and is affected by other external factors on the exploration parameters except the rain climate; when the error verification evaluation index is greater than or equal to the preset threshold, it indicates that the rain climate has indeed caused a significant impact on the exploration, indicating that the result obtained by the climate impact evaluation model is correct.

[0084] Specifically, the error verification evaluation index is calculated by the following formula:

[0085] PG wc = e1 × J y + e2 × C k + e3 × H d

[0086] In the above formula, PG wc represents the error verification evaluation index, and e1, e2, and e3 are different preset proportional coefficients of the instrument precision offset coefficient, the exploration seismic wave propagation influence coefficient, and the geochemical property change coefficient respectively.

[0087] S5. Retain the part where the evaluation result of the climate impact evaluation model is the same as the verification result of the error verification evaluation model to obtain the verified retained data, that is, the retained data is: retain the part where the error verification evaluation model verifies that the climate impact evaluation model is evaluated correctly.

[0088] S6. Obtain the exploration plan, adjust the exploration plan according to the verified retained data, and add the corresponding parts of the results that are not retained to the exploration plan after subsequent re-detection. Specifically:

[0089] Before preparing for the development of the exploration area, it is usually necessary to formulate a preliminary exploration plan to plan future exploration activities. When it is determined that the climate impact evaluation model is inaccurate according to the error verification evaluation model, collect the exploration and development data under subsequent non-rainy climates to analyze and exclude the factors causing changes in exploration data except the rain climate, and at the same time adjust the actual exploration plan, such as delaying the exploration before excluding the factors.

[0090] S7. Conduct exploration and development according to the adjusted exploration plan, monitor the exploration and development process in real time, and feed it back to the database to provide data support for subsequent similar exploration situations.

[0091] By fusing the exploration data in the non-rainy climate of the target area, a climate impact assessment model is constructed. The purpose is to prevent the influence of trace rainfall or rainfall several kilometers away, infer the exploration data situation under rainy climate, compare it with the existing data based on this, and determine whether the rainy climate has an impact on the exploration data. After the judgment is completed, it is deduced and verified through the impact of rain on the exploration process. By first deducing the past data, the cost of data collection required to judge the degree of influence of rain on exploration data is reduced. Then, through the deduction of various information changes that may be caused by rainy climate, the deduction result is compared and verified with the actual situation to determine whether the exploration data is only affected by rainy climate, so as to facilitate the adjustment of subsequent geological exploration strategies, improve the accuracy and safety of exploration, and at the same time optimize the database for subsequent exploration operations.

[0092] As Figure 2 shown, the present invention also provides an exploration and development data fusion system, including a data collection module, a climate impact assessment module, a comparison and correction module, an error verification and assessment module, a processing module, an adjustment module, and a monitoring and feedback module that are connected in sequence; the data collection module is used to execute the content described in step S1 and collect historical exploration and development data in the non-rainy climate and rainy climate of the target area; the climate impact assessment module is used to execute the content described in step S2 and construct a climate impact data assessment model; the comparison and correction module is used to execute the content described in step S3 and provide correction suggestions for the exploration and development data in rainy climate according to the accuracy assessment result in the climate impact assessment module; the error verification and assessment module is used to execute the content described in step S4 and construct an error verification and assessment model based on the instrument impact degree in rainy climate and the measurement error amount that may be caused by the change of the groundwater level, and verify the correction suggestions in the comparison and correction module; the processing module is used to execute the content described in step S5 and retain the same part of the verification results of the two data models before and after to obtain the verified retained data; the adjustment module is used to execute the content described in step S6 and obtain the exploration plan, adjust the exploration plan according to the verified retained data, and add the corresponding part without the retained result to the exploration plan after subsequent re-detection; the monitoring and feedback module is used to execute the content described in step S7, conduct exploration and development according to the adjusted exploration plan, monitor the exploration and development process in real time, and feedback to the database to provide data support for subsequent similar exploration situations.

[0093] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than a limitation on the protection scope of the present invention. Any simple modification or equivalent replacement of the technical solution of the present invention by those of ordinary skill in the art does not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for integrating exploration and development data, characterized in that, It includes the following steps: S1. Collect historical exploration and development data under non-rainy climate in the target area; S2. Construct a climate impact assessment model based on the historical exploration and development data collected in step S1; S3. Obtain exploration and development data under rainy climate in the target area, use the climate impact assessment model obtained in step S2 to evaluate the accuracy of the exploration and development data under rainy climate, and determine whether to give corrective suggestions according to the evaluation results; S4. Establish an error verification assessment model to verify the evaluation results of the climate impact assessment model in step S3; S5. Retain the parts where the evaluation results of the climate impact assessment model are the same as the verification results of the error verification assessment model to form retained data; S6. Obtain an exploration plan, adjust the exploration plan through the retained data, re-detect the un-retained part of the data, and add it to the exploration plan; S7. Conduct exploration and development according to the adjusted exploration plan, monitor the exploration and development process in real time, and feed it back to the database.

2. The exploration and development data fusion method according to claim 1, characterized in that, S2 specifically includes the following steps: S201. Summarize and integrate the historical exploration and development data under non-rainy climate, and perform standardization processing; S202. Use the standardized historical exploration and development data to construct a climate impact assessment model to obtain the theoretical values of the occurrence probabilities of various exploration and development data under rainy climate.

3. The exploration and development data fusion method according to claim 2, wherein, S202 specifically includes the following steps: S2021. Obtain a set of candidate values through the Metropolis-Hastings algorithm or Gibbs; S2022. Select one from the standardized historical exploration data as the initial value, and then combine it with the set of candidate values obtained in S2021 to obtain the values of various exploration and development data under rainy climate through the Markov chain Monte Carlo method; S2023. Obtain the theoretical values of the occurrence probabilities of various exploration and development data under rainy climate at the stationary distribution through Bayesian statistical methods.

4. A method for exploration and development data fusion according to claim 1, wherein S3 specifically includes the following steps: S301. Calculate the coincidence degree between the value obtained from the climate impact assessment model and the exploration and development data under rainy climate in the target area obtained; S302. Compare the coincidence degree calculated in step S301 with the preset accuracy threshold. When the coincidence degree is less than the preset accuracy threshold, it means that the exploration and development data under rainy climate is inaccurate and the exploration and development data under rainy climate needs to be corrected. When the coincidence degree is greater than or equal to the preset accuracy threshold, it means that the exploration and development data under rainy climate is accurate and there is no need to correct the exploration and development data under rainy climate.

5. The exploration and development data fusion method according to claim 4, wherein The coincidence degree in step S301 is specifically calculated by the following formula: In the above formula, A i represents the coincidence degree of the i-th exploration and development data under the rain climate measured in the target area, represents the value obtained by the i-th exploration and development data in the target area through the climate impact assessment model, represents the value measured by the i-th exploration and development data under the rain climate in the target area, represents the average coincidence degree of the exploration and development data under the rain climate in the target area, and n represents the types of data in the exploration and development data in the target area.

6. The exploration and development data fusion method according to claim 1, wherein S4 specifically includes the following steps: S401. Construct an error verification assessment model, and the error verification assessment model includes an instrument precision offset coefficient, an exploration seismic wave propagation influence coefficient, and a geochemical property change coefficient; S402. According to the error verification assessment model, calculate the error verification assessment index, and evaluate the evaluation results of the climate impact assessment model in step S3 according to the error verification assessment index.

7. A method for fusing exploration and development data according to claim 6, characterized in that, The instrument precision offset coefficient in S401 is calculated by the following formula: In the above formula, J y represents the instrument precision offset coefficient, y represents the numbers of different instruments, y = 1, 2, 3, 4, …, u, where u is a positive integer, and S y is the instrument measurement data under rainy weather, represents the average value of the detection data of different instruments under non-rainy weather.

8. A method for fusing exploration and development data according to claim 6, characterized in that, The exploration seismic wave propagation influence coefficient in S401 is specifically calculated by the following formula: In the above formula, C k represents the influence coefficient of exploration seismic wave propagation, and H sw represents the groundwater level height during exploration under rainy climate, represents the target area water level standard value obtained from the annual average groundwater level height of the recorded target area.

9. A method for fusing exploration and development data according to claim 6, characterized in that, The coefficient of geochemical property change in S401 is obtained by the following formula: In the above formula, H d represents the coefficient of change in geochemical properties, x represents the number obtained by sampling at different heights of the well logging, x = 1, 2, 3, 4, …, j, where j is a positive integer, and R x represents the soil chemical composition measured at different heights of the well logging, and R ′ x represents the soil chemical composition measured under the daily non-rainy climate at the corresponding height x obtained from the historical data.

10. The exploration and development data fusion method according to claim 6, wherein The error verification and evaluation index in S402 is specifically calculated by the following formula: PG wc = e1 × J y + e2 × C k + e3 × H d In the above formula, PG wc represents the error verification and evaluation index, and e1, e2, and e3 are different preset proportionality coefficients of the instrument precision offset coefficient, the exploration seismic wave propagation influence coefficient, and the geochemical property change coefficient, respectively.

11. A method for fusing exploration and development data according to claim 6, characterized in that, The specific method for the error verification and evaluation module in S402 to evaluate the evaluation result of the climate impact assessment model in S3 is as follows: compare the error verification and evaluation index with a preset threshold. When the error verification and evaluation index is less than the preset threshold, it indicates that the evaluation result of the climate impact assessment model is incorrect. When the error verification and evaluation index is greater than or equal to the preset threshold, it indicates that the evaluation result obtained by the climate impact assessment model is correct.

12. A method for integrating exploration and development data according to claim 1, characterized in that, The historical exploration and development data under non-rainy climate obtained in S2 includes seismic wave information, formation information, electromagnetic information, and drilling information.

13. A method for fusing exploration and development data according to claim 12, characterized in that, The formation information includes rock samples, core analysis, and rock physical property data.

14. A method for fusing exploration and development data according to claim 12, characterized in that, The drilling information includes borehole logs, wellbore profile data, wellbore temperature, and pressure data.

15. A system for using the exploration and development data fusion method according to any one of claims 1-14, characterized in that, It includes a data collection module, a climate impact assessment module, a comparison and correction module, an error verification and evaluation module, a processing module, an adjustment module, and a monitoring and feedback module connected in sequence. The data collection module is used to execute the content described in S1. The climate impact assessment module is used to execute the content described in S2. The comparison and correction module is used to execute the content described in S3. The error verification and evaluation module is used to execute the content described in S4. The processing module is used to execute the content described in S5. The adjustment module is used to execute the content described in S6. The monitoring and feedback module is used to execute the content described in S7.

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