Method and device for analyzing land value of oil field, electronic equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of oil field land value evaluation, and discloses an oil field land land value analysis method and device, electronic equipment, a storage medium and a computer program product.The method comprises the steps that influence factors of oil field land value analysis are extracted on the basis of a knowledge graph fusion technology; the influence factors comprise land types, reserve conditions, ground development difficulty, land input and land output; determining a comprehensive evaluation coefficient of the influence factor based on an analytic hierarchy process and expert scoring; and analyzing the oil field land use value of the oil field land based on the comprehensive evaluation coefficient. According to the method, the efficiency and accuracy of evaluating the land value of the oil field are improved, a solid foundation is laid for development of the oil field in the fields of strategic and land resource planning and utilization, the automatic evaluation process is realized, and the evaluation period is remarkably shortened.

Description

Technical Field

[0001] This invention relates to the field of oilfield land value evaluation technology, and in particular to a method, apparatus, electronic device, storage medium and computer program product for oilfield land value analysis. Background Technology

[0002] Given the increasing difficulty in acquiring new construction land and the rising costs and low utilization efficiency of existing land, how to make a comprehensive, objective and effective evaluation of land assets, rationally develop and utilize land assets, achieve economical and intensive land use, and efficiently allocate development resources has become an important issue worthy of consideration.

[0003] In the post-production evaluation stage of oil and gas, the land value accounting lacks quantitative indicators and evaluation methods, resulting in low reliability of land value evaluation. The evaluation and optimization of land resources require a large amount of information as support. Although the conditions for basic information resources in oilfields are in place, including data on geology, surface conditions, new energy sources, oil and gas exploration, oil and gas reservoir evaluation, and oil and gas production, there is a lack of scientific tools. This makes it impossible to realize land use planning, land value analysis and evaluation, and optimization of land use indicators, which is not conducive to the company's refined land use decision-making.

[0004] Current research on oilfield land value assessment mainly revolves around core issues, including land use efficiency, land value assessment methods, and optimal land allocation. Although some research and practical experience exist, several problems remain, such as imprecise assessment methods and difficulties in data acquisition.

[0005] 1) Oilfields face many challenges in land resource management, such as a lack of quantitative evaluation indicators, imprecise evaluation methods, and a lack of scientific assessment tools despite abundant basic information resources.

[0006] 2) Utilizing advanced technologies and methods for land evaluation, such as remote sensing technology and three-dimensional geological modeling technology, can more accurately quantify and assess the value of land resources.

[0007] Currently, domestic research methods and technologies still have certain drawbacks and limitations in some aspects, specifically in the following areas:

[0008] 1) Inaccurate assessment methods: Existing oilfield land value assessment methods are often based on traditional statistical analysis and experience-based judgment, lacking scientific rigor and accuracy. This can lead to significant errors in the assessment results, failing to provide a reliable basis for decision-making.

[0009] 2) Difficulty in data acquisition: The valuation of oilfield land requires a large amount of basic data as support. However, in practice, there are often problems such as missing or inaccurate data, and multiple data sources. This not only affects the accuracy of the valuation but also increases the difficulty and cost of the valuation.

[0010] 3) Lack of unified evaluation standards: Currently, there is no unified evaluation standard and methodology system for oilfield land value assessment. This leads to significant differences in assessment results between different oilfields, making effective comparison and analysis impossible.

[0011] Therefore, it is of great significance to develop a comprehensive evaluation and analysis method for oilfield land value based on visualization, expert scoring, and the analytic hierarchy process. Summary of the Invention

[0012] To address the above problems, this invention provides a method and apparatus for analyzing the value of oilfield land use. The technical solution provided by this invention is as follows:

[0013] In a first aspect of the present invention, a method for analyzing the value of oilfield land is provided, the method comprising:

[0014] Based on oil and gas reserves data, oil and gas production data, land types, land ownership, and geospatial information, a knowledge graph network reflecting the factors influencing the value of oilfield land is constructed.

[0015] Based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area, the influencing factors of oilfield land value analysis are extracted. The influencing factors include land type, reserve conditions, surface development difficulty, land input and land output.

[0016] The comprehensive evaluation coefficient of the influencing factors was determined based on the analytic hierarchy process and expert scoring.

[0017] The value of oilfield land is analyzed based on the comprehensive evaluation coefficients mentioned above.

[0018] Furthermore, the actual oilfield land use evaluation data for the target area includes target oilfield reserve data, target oil and gas production data, target surface engineering data, target land ownership data, target well location data, target land business data, and target land type.

[0019] Furthermore, based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area, the influencing factors for oilfield land value analysis are extracted, including:

[0020] Using natural language processing technology, key entities, relationships, and attributes are extracted from the actual oilfield land use evaluation data of the target area to construct an oilfield land use value knowledge base;

[0021] Based on a knowledge graph network reflecting the influencing factors of oilfield land value, this study analyzes the knowledge base of oilfield land value using the knowledge graph network reflecting the influencing factors of oilfield land value and extracts the influencing factors for oilfield land value analysis.

[0022] Furthermore, based on the analytic hierarchy process (AHP) and expert scoring, the comprehensive evaluation coefficient of the influencing factors is determined, including:

[0023] The first influencing factor is set as the influencing factor in the analysis of oilfield land use value.

[0024] Each primary impact factor is decomposed into multiple secondary impact factors, and a hierarchical analysis model is established.

[0025] In the hierarchical analysis model, the top-level objective is to evaluate the value of oilfield land; the criterion layer includes the first influencing factor, and the sub-criterion layer includes the second influencing factor.

[0026] The weight coefficients corresponding to each second influencing factor in the analytic hierarchy process were determined using the analytic hierarchy process.

[0027] The weight coefficients for each second influencing factor in the analytic hierarchy process were determined by using expert scoring.

[0028] The weighting coefficients determined by the analytic hierarchy process (AHP) and the weighting coefficients determined by the expert scoring method are weighted and calculated to determine the comprehensive evaluation coefficient for each influencing factor.

[0029] Furthermore, the analytic hierarchy process (AHP) is used to determine the weight coefficients corresponding to each second influencing factor in the AHP model, including:

[0030] In the hierarchical analysis model, the multiple second-influence factors corresponding to each first-influence factor are compared and scored pairwise to create a comparison matrix;

[0031] The consistency of the comparison matrix is ​​tested using consistency indices and random consistency ratios.

[0032] The comparison matrix after consistency testing is normalized and the eigenvector is calculated to determine the weight coefficient corresponding to each second influencing factor.

[0033] Furthermore, an expert scoring method is used to determine the weight coefficients corresponding to each second influencing factor in the analytic hierarchy process (AHP) model, including:

[0034] In the hierarchical analysis model, the multiple second-influence factors corresponding to each first-influence factor are compared pairwise, and a comparison matrix is ​​created based on the expert's judgment score.

[0035] For each comparison matrix, normalize the data and calculate the eigenvectors to determine the weight coefficients;

[0036] The consistency of the normalized comparison matrix is ​​tested using a consistency index and a random consistency ratio. When the random consistency ratio is less than 0.10, the weight coefficient is the weight coefficient corresponding to each second influencing factor.

[0037] Furthermore, the second influencing factors corresponding to the land types include: mining land, unused land, grassland, forest land, ordinary cultivated land, basic farmland, special land, areas outside mining rights, land within ordinary urban planning areas, land within the Xinjiang Production and Construction Corps boundary, land within the capital city planning area, railway and highway facilities, core areas of nature reserves, non-core areas of nature reserves, and water conservancy facilities such as rivers and reservoirs.

[0038] or / and,

[0039] The second influencing factor corresponding to the reserve conditions includes the favorable area of ​​reserves, the marginal area of ​​rolling development, and the unexplored area;

[0040] or / and,

[0041] The second influencing factor corresponding to the difficulty of surface development includes areas with oilfield development and production support within ≤50 kilometers, areas far from oilfield development and production support within ≤50 kilometers and less than 100 kilometers, areas far from oilfield development and production support within ≤100 kilometers and less than 200 kilometers, and areas far from oilfield development and production support within ≤200 kilometers.

[0042] or / and,

[0043] The second influencing factor corresponding to the first influencing factor, land input, includes temporary land use input costs, long-term land use input costs, holding costs, and disposal and waste costs.

[0044] or / and,

[0045] The second influencing factor corresponding to the first influencing factor, land output, includes land output value and land revenue.

[0046] Furthermore, the weighting coefficients determined by the analytic hierarchy process (AHP) and the expert scoring method are weighted and calculated to determine the comprehensive weighting coefficient for each influencing factor.

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] In the formula, Q 土地地类 Q represents the comprehensive weighting coefficient of land categories; 储量条件 Q represents the comprehensive weighting coefficient of factors influencing reserves conditions; 地面开发难度 Q represents the comprehensive weighting coefficient of factors influencing the difficulty of ground development; 土地投入 Q represents the comprehensive weighting coefficient of land input influencing factors; 土地产出 The comprehensive weighting coefficient of land output influencing factors, x 1i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in land use classification; y 1i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in the land category; x 2i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in the reserve conditions; y 2i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in the reserve conditions; x 3i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process (AHP) in the difficulty of ground development; y 3i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in the difficulty of ground development; x 4i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in land input; y 4i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in land investment; x 5i y represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in land output. 5i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in land output.

[0053] Furthermore, the analysis of oilfield land value based on the aforementioned comprehensive weighting coefficient includes:

[0054] The sum of land type, surface development difficulty, and land input evaluation value is the oilfield land input evaluation value;

[0055] The sum of the output evaluation values ​​of reserves and land output is the output evaluation value of oilfield use;

[0056] The ratio of the evaluated value of oilfield land input to the evaluated value of oilfield land output is the land value evaluation ratio.

[0057] Based on the aforementioned land value evaluation ratio, the value of oilfield land is determined as high-efficiency land, medium-efficiency land, low-efficiency land, or negative-efficiency land.

[0058] Furthermore, the formula for calculating the land value assessment ratio is as follows:

[0059]

[0060] in,

[0061] Input evaluation value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0062] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%

[0063] Furthermore,

[0064] Land with a land value ratio greater than or equal to 5 is considered high-efficiency land.

[0065] Land with a land value assessment ratio greater than or equal to 3 and less than 5 is considered medium-efficiency land.

[0066] Land with a land value ratio greater than or equal to 1 but less than 3 is considered inefficient land.

[0067] When the land value assessment ratio is less than 1, it is considered negative land.

[0068] In a second aspect of the invention, an apparatus for analyzing the value of oilfield land is provided, the apparatus comprising,

[0069] The building unit is used to construct a knowledge graph network that reflects the influencing factors of oilfield land value based on oil and gas reserve data, oil and gas production data, land type, land ownership and geospatial information.

[0070] The extraction unit is used to extract the influencing factors of oilfield land value analysis based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area. The influencing factors include land type, reserve conditions, surface development difficulty, land input and land output.

[0071] A unit is defined to determine the comprehensive evaluation coefficient of the influencing factors based on the analytic hierarchy process and expert scoring.

[0072] The analysis unit is used to analyze the value of oilfield land based on the comprehensive evaluation coefficient.

[0073] In a third aspect of the invention, an electronic device is provided, the electronic device comprising at least one processor and at least one memory, the memory being data-connected to the processor, wherein...

[0074] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0075] In a fourth aspect of the invention, a computer-storeable medium is provided, characterized in that the storage medium stores computer instructions, which, when executed by a processor, specifically perform the steps in the method described above.

[0076] In a fifth aspect of the invention, a computer program product is provided, comprising computer instructions, characterized in that, when the computer instructions are executed by a processor, they specifically perform the steps in the method described above.

[0077] The technical effects and advantages of this invention are as follows:

[0078] 1) Improved Efficiency of Oilfield Land Valuation: The application of the method of this invention significantly improves the efficiency of oilfield land valuation. Traditional assessment methods typically require a significant amount of time and manpower, while this invention automates the assessment process, significantly shortening the assessment cycle. Preliminary statistics show that project assessment time has been reduced by an average of 15-30 days, and assessment costs have been reduced by 10%-20%.

[0079] 2) Improved assessment accuracy: This work employs a multi-source data fusion approach, fully considering five major influencing factors: land type, land reserves, surface development difficulty, land input, and land output. Quantification and weighting were performed using the analytic hierarchy process (AHP) and expert scoring. This comprehensive assessment method makes the results more accurate and reliable.

[0080] 3) Reduced labor costs: The automated evaluation process of this innovation reduces reliance on manual labor and reduces a lot of tedious data collection and processing work, thereby reducing additional costs caused by decision delays.

[0081] 4) Enhance decision support capabilities: Through visualization analysis tools, decision-makers can more intuitively understand the value distribution and influencing factors of oilfield land, and can more clearly understand the value differences of different plots in the oilfield, providing new ideas and methods for oilfield land management.

[0082] 5) Optimize resource allocation: By applying this achievement, the value of different plots in an oilfield can be assessed more accurately, thereby optimizing resource allocation based on strategic needs and development plans, and maximizing resource utilization.

[0083] 6) Improve land use efficiency: Accurate land value assessment can help oil fields to conduct land use planning more scientifically, improve land use efficiency, and reduce land waste.

[0084] 7) Significant impact on strategic oilfield development and land resource planning and utilization: The successful development and application of this achievement not only improves the efficiency and accuracy of oilfield land value assessment but also lays a solid foundation for the development of oilfields in strategic oilfield development and land resource planning and utilization. Through continuous technological innovation and research and development, it can maintain a leading position in fierce market competition and provide strong support for future sustainable development.

[0085] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0086] Figure 1 This is a flowchart of an oilfield land value analysis provided in an embodiment of this application;

[0087] Figure 2 This is a diagram of the oilfield land value analysis device provided in the embodiments of this application;

[0088] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] To address the shortcomings of existing technologies, this invention discloses a method for analyzing the value of oilfield land, such as... Figure 1 As shown, the method includes,

[0091] Step 1: Based on oil and gas reserve data, oil and gas production data, land type, land ownership, and geospatial information, construct a knowledge graph network that reflects the factors influencing the value of oilfield land use;

[0092] Step 2: Based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area, extract the influencing factors of oilfield land value analysis. The influencing factors include land type, reserve conditions, surface development difficulty, land input and land output.

[0093] Step 3: Determine the comprehensive evaluation coefficient of the influencing factors based on the analytic hierarchy process and expert scoring;

[0094] Step 4: Analyze the value of oilfield land based on the comprehensive evaluation coefficients.

[0095] In a specific embodiment of the present invention, the combination of data affecting the evaluation of oilfield land value and knowledge graph technology in step 1 is explained as follows:

[0096] 1) In the process of evaluating the value of oilfield land, in order to comprehensively and accurately identify all the key factors affecting its value, knowledge graph fusion technology is introduced. This technology can integrate and analyze massive and heterogeneous data sources, including oil and gas reserve data, oil and gas production data, land type data, land ownership and geospatial information, etc. Through semantic association and logical reasoning, a knowledge network that comprehensively reflects the factors affecting the value of oilfield land is constructed.

[0097] ① Oil and gas reserve data: proven oil, proven gas, controlled oil, controlled gas, predicted oil, and predicted gas, including but not limited to attribute information and spatial information, such as graphic data of the specific geographical extent of proven oil. This data, as a direct determinant of reserve conditions, is a quantifiable and specific indicator.

[0098] ② Oil and gas production data: daily output, monthly output, annual output, international oil price, and internal oil price within the oil field, including but not limited to single-well production reports or other generated data for each land parcel. This data serves as the basis for estimating the output value of the land.

[0099] ③ Land category data: mining land, unused land, grassland, forest land, cultivated land, basic farmland, special-use land, land within ordinary urban planning areas, land within the Xinjiang Production and Construction Corps boundary, land for railways, highways and other facilities, nature reserve areas, etc. The above data includes, but is not limited to, attribute information and graphic boundary data. This data serves as one of the spatial evaluation factors for land value assessment.

[0100] ④ Land ownership data: oilfield-owned land and non-oilfield-owned land. This data serves as one of the spatial evaluation factors for the value of oilfield land.

[0101] ⑤ Well location data: Information such as well number, well type, and spatial coordinates. This data can be correlated and mapped with other types of data through spatial and attribute relationships, enriching the basic data for land value assessment models.

[0102] ⑥ Surface engineering data: mainly refers to the vector graphics and attribute information data of oilfield surface facilities (pipelines, stations, etc.), which serve as a reference for the difficulty of surface development.

[0103] ⑦ Land business data: Temporary land use fees, long-term land use fees, urban land use tax, land lease fees, land reclamation fees, and waste disposal fees, etc. These serve as fundamental data for land investment and provide a reference for land value assessment and analysis.

[0104] 2) Collect and organize data from various sources, including but not limited to oilfield reserve characteristics, engineering facility layout, land ownership and land use, and land classification. In some embodiments, data sources also include environmental protection requirements and policies and regulations. Subsequently, using Natural Language Processing (NLP) technology, these data are preprocessed to extract key entities, relationships, and attributes, and a preliminary knowledge graph of oilfield land value is constructed.

[0105] By extracting key entities, relationships, and attributes from data such as oil and gas reserves, oil and gas production, land types, land ownership, well locations, and surface engineering, and establishing correlations, a knowledge graph of all land elements is constructed to facilitate the development of subsequent land value evaluation and analysis models.

[0106] 3) Through knowledge graph fusion technology, we effectively integrate information from different data sources, eliminating redundancy and contradictions to form a unified, accurate, and comprehensive knowledge base for oilfield land value. In this process, we can further focus on core areas such as geological conditions, extraction costs, oil and gas resource quality, market demand, and environmental protection requirements, ensuring that all key factors are accurately identified and incorporated into the evaluation system.

[0107] By combining data from oilfield land value assessment with knowledge graphs, we can systematically identify various factors influencing oilfield land value and reveal the complex relationships between them. This provides a solid theoretical foundation and data support for subsequent analytic hierarchy process (AHP) and expert scoring methods. This process not only improves the comprehensiveness and accuracy of the assessment but also enhances the interpretability and credibility of the results.

[0108] The following steps are taken to construct a knowledge graph network that reflects the factors influencing the value of oilfield land use:

[0109] Step 101: Construct a structured graph data model

[0110] Objective: To establish a graph data structure that clearly defines entities such as land use types, geological information, engineering information, and new energy information, as well as their relationships. This model is primarily used to describe and organize entities and their relationships within the data for in-depth analysis. Furthermore, it utilizes knowledge graph technology to integrate and analyze oilfield land use data from different sources (such as reserve data and production data) to identify and extract factors influencing the value of oilfield land use.

[0111] Implementation: A graph data model is constructed by defining entities, relationships, and graph model attributes. Entities include land parcels and geological information, while relationships include the association between land parcels and geological information. A knowledge graph is then built using technologies such as natural language processing to extract key entities and relationships, and these are integrated into a knowledge graph network.

[0112] Relationship Function: Structured graph data models provide a structured way to represent oilfield land-related data, laying the foundation for subsequent analysis and mining. They also provide a comprehensive knowledge framework and contextual understanding for oilfield land value analysis, helping to identify and extract factors influencing oilfield land value. Structured graph data models can be seen as a technical means or step in constructing knowledge graph networks. They provide a basic, structured framework for knowledge graphs, enabling the effective representation of entities and relationships related to oilfield land. Knowledge graph networks, on the other hand, further integrate unstructured data and information from other sources based on structured graph data models, forming a more comprehensive and complex knowledge system that reflects the value of oilfield land.

[0113] Specific steps: Research the methods and rules for establishing graph data models, the data structure for representing the relationships between business entities, and in the land all-element association application model, study how to use graph data models to express the relationships between entities such as land parcels, geological information, engineering information, and new energy information.

[0114] The relationship between structured graph data models and knowledge graphs mainly includes the following two points: First, graph data models can serve as an important component of knowledge graphs, providing a data foundation and detailed structure through specific structured information (such as entities and relationships). Second, knowledge graph networks integrate information and relationships extracted from multiple data sources, offering a more macroscopic perspective for analyzing the influencing factors of oilfield land value. Knowledge graphs can enhance their accuracy and depth through data from structured graph data models.

[0115] In the process of building a structured graph data model, the expression of the graph data model can be defined and enriched in the following ways:

[0116] 1) Entity definition:

[0117] Plot: Represented as a node in the graph, its attributes include plot size, location, land type (such as arable land, forest land), and current utilization status.

[0118] Geological information: Also a node, its attributes include geological structure, mineral composition, historical mining information, etc.

[0119] Project Information: As a node, attributes include project type (such as drilling, surface construction), budget, and construction period.

[0120] New energy information: This involves new energy projects (wind power, solar power, etc.), and the node attributes include equipment type, energy efficiency, installation date, etc.

[0121] 2) Relationship definition:

[0122] Land parcels and geological information: The specific geological attributes of land parcels are represented by boundary connections.

[0123] Land parcel and project information: Edges represent the implementation status of a specific project on a specific land parcel.

[0124] Geological information and engineering information: Edges represent engineering adaptability or engineering limitations under specific geological conditions.

[0125] Land parcel and land investment information: The edge represents the various investments or resource inputs made on the land parcel.

[0126] Land input and land output: The boundary reflects the direct relationship between input and output, that is, the expected output benefits after investment or resource input.

[0127] Land parcel and land output information: The edge is directly related to the final output benefits of the land parcel, including economic benefits, social benefits and environmental benefits.

[0128] 3) Graph model properties:

[0129] Edge weights can represent the strength or importance of a relationship, such as the severity of the impact of geological conditions on an engineering project.

[0130] Directionality: Some relationships may have directionality; for example, engineering projects can affect the state of a land parcel.

[0131] 4) Query and analysis:

[0132] Use the graph query language Cypher to query specific patterns, such as finding all plots of land that cannot be developed on a large scale due to geological limitations.

[0133] Graph algorithms are applied to determine the optimal route for allocating engineering resources through shortest path analysis.

[0134] Step 102: Construction of Unstructured Data Analysis Algorithms

[0135] Objective: To process and analyze unstructured data from text, images, and professional models, and construct a knowledge graph through entity recognition and relation extraction. Based on the constructed knowledge graph network, subsequent steps will employ the analytic hierarchy process (AHP) and expert scoring to extract the main factors influencing the value of oilfield land use.

[0136] The implementation process includes steps such as data collection, preprocessing, entity recognition, relation extraction, and knowledge fusion. These steps help extract structured information from unstructured data and construct a knowledge graph. Using the knowledge graph constructed from unstructured data analysis, the comprehensive evaluation coefficients of each factor are determined through the analytic hierarchy process (AHP) and expert scoring.

[0137] Relational role: It enables the systematization and integration of knowledge extracted from unstructured data, providing support for subsequent analysis, and through a systematic knowledge graph, it provides a foundation and reference for the analysis of oilfield land value.

[0138] The relationship between unstructured data analysis algorithm research and knowledge graph networks mainly includes the following two points: First, unstructured data analysis algorithm research is a prerequisite for knowledge graph network construction. By processing unstructured data, extracting key information, and integrating it into a knowledge graph, a basic data framework is constructed. Second, the extraction of knowledge graph network data is a process of in-depth analysis and comprehensive evaluation based on the already constructed knowledge graph. This step utilizes existing knowledge graph data to identify and evaluate factors affecting the value of oilfield land use.

[0139] This research explores a method for constructing knowledge graphs for unstructured data, such as text, images, and professional models, based on entity recognition and relation extraction results. The method involves constructing knowledge graphs through the analysis of unstructured data. The following is an overview of the process of building a knowledge graph and the data types that may be included within it:

[0140] 1) Data collection:

[0141] Collect relevant unstructured data, including text (research reports, news articles, regulatory documents, etc.), images (satellite images, on-site photos, etc.), and professional models (such as geological model data).

[0142] 2) Data preprocessing:

[0143] The text is segmented, stop words are removed, and the text is standardized.

[0144] Image data undergoes preprocessing such as formatting, noise reduction, and feature extraction.

[0145] Perform format conversion and standardization on professional model data.

[0146] 3) Entity recognition:

[0147] Use natural language processing techniques to identify key entities in text, such as location names, equipment types, and project names.

[0148] Image recognition technology is used to identify specific symbols, geographical features, etc. in images.

[0149] Key parameters and features are extracted from a professional model to form entities.

[0150] 4) Relation extraction:

[0151] Extract relationships between entities (such as "located in", "used", "belongs to", etc.) from text using pattern matching or machine learning algorithms.

[0152] Identify spatial, functional, and other relationships between entities from image and model data.

[0153] 5) Knowledge integration and consolidation:

[0154] It integrates entities and relationships extracted from different data sources to resolve ambiguity and redundancy issues in entity recognition.

[0155] Construct a graph to store entities and relationships in the form of a graph.

[0156] 6) Optimization and updating of the knowledge graph:

[0157] The knowledge graph is regularly updated and expanded to reflect new discoveries and changes.

[0158] Apply graph analysis techniques for knowledge discovery and verification, and optimize knowledge structure.

[0159] Data included in the knowledge graph:

[0160] Entities: The entities involved, including land parcels, reserves, and surface facilities.

[0161] Attributes: Descriptive data related to entities, such as the area, category, and reserves of land parcels.

[0162] Relationship: A connection between entities that represents a logical or physical relationship, such as "located in", "used", or "belongs to".

[0163] Event: Important activities that occur at a specific time, such as the start and end dates of land development, contract signing, etc.

[0164] In a specific embodiment of the present invention, in step 2, based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area, the influencing factors for oilfield land value analysis are extracted. The actual oilfield land evaluation data of the target area includes target oilfield reserve data, target oil and gas production data, target surface engineering data, target land ownership data, target well location data, target land business data, and target land type. The specific steps are as follows.

[0165] Step 201: Using natural language processing technology, extract key entities, relationships and attributes from the actual oilfield land use evaluation data of the target area, and construct an oilfield land use value knowledge base;

[0166] Step 202: Based on knowledge graph fusion technology, analyze the knowledge base of oilfield land value using a knowledge graph network that reflects the influencing factors of oilfield land value, and extract the influencing factors for oilfield land value analysis. The specific operation is as follows:

[0167] Building a correlation analysis model for business attributes based on spatial and temporal dimensions

[0168] A correlation analysis model is constructed, slicing data items in the knowledge graph into datasets based on three categories: time, space, and business scenario. Then, the support (support(A) = count(A) / count(dataset) = P(A)) and confidence (Confidence = P(A&B) / P(A)) are calculated. Frequent sets are mined from the dataset using classic frequent set mining algorithms, and association rules are summarized based on these frequent sets. This invention employs the FP-growth algorithm for frequent set mining. The FP-growth algorithm is a more efficient algorithm than Aprili for discovering frequent itemsets in a dataset. It uses a tree structure called an FP-tree (Frequent Pattern Tree) to compress the dataset.

[0169] The process of constructing a spatial-temporal dimension oilfield land use value assessment and analysis model involves three main parts: data processing, frequent set mining, and association rule generation. These steps can help reveal implicit relationships and patterns in the knowledge graph, providing data support for decision-making. The following is a detailed description of the construction process and the expression of the association analysis model:

[0170] The process of building an association analysis model:

[0171] 1) Dataset slicing:

[0172] The data in the knowledge graph is categorized and sliced ​​based on time, space, and business scenario dimensions. For example, the data can be divided into specific time periods (by quarter), specific regions (drilling blocks), and specific business scenarios (mining types). By conducting more detailed correlation analysis on the data, behavioral patterns or regularities under specific conditions can be identified.

[0173] 2) Frequent set mining:

[0174] The FP-growth algorithm is used to process the dataset and identify frequently occurring itemsets, i.e., frequent sets.

[0175] Frequent sets are itemsets that appear in a dataset with a probability higher than a set threshold (minimum support threshold).

[0176] Constructing the initial FP-tree: First, construct the FP-tree by scanning the database and recording the occurrence count of each item. Remove infrequent items and sort the frequent items in descending order of frequency.

[0177] Create the tree header: record the linked list pointers for each item in the FP-tree.

[0178] FP-trees are built for each transaction in the database: for each transaction, they are sorted in descending order of global frequency and then inserted into the tree. If a part of the path that shares a prefix (a set of identical items) already exists, the count is incremented; otherwise, a new node is created.

[0179] Mining frequent itemsets using FP-trees: Starting with each item (in the order of the header), construct a conditional pattern base, then construct a conditional FP-tree, and recursively mine frequent itemsets.

[0180] The use of the FP-growth algorithm avoids generating a large number of candidate options.

[0181] 3) Calculate support and confidence:

[0182] Support: The frequency with which an itemset appears in all transactions. The formula is:

[0183]

[0184] Confidence: The conditional probability that itemset B will also occur given that itemset A has occurred. The formula is as follows:

[0185]

[0186] 4) Association rule generation:

[0187] Based on the mined frequent sets and the calculated support and confidence scores, potential association rules are generated. These rules help to understand how specific data items are associated with other items.

[0188] 5) Model validation and optimization:

[0189] Use techniques such as cross-validation to validate association rules to ensure their effectiveness and accuracy.

[0190] Adjust the support and confidence thresholds according to business needs to optimize model performance.

[0191] Expression or description of the oilfield land value assessment and analysis model:

[0192] Model Description: The oilfield land value assessment and analysis model is a statistical model that derives strong association rules between itemsets by identifying frequent co-occurrence patterns among data items. The model utilizes the Eclat algorithm to mine meaningful relationships from large-scale datasets.

[0193] Expression example: If there are rules

[0194] With a support of 0.5% and a confidence level of 70%, this means that the probability of land type A and oil and gas reserves B occurring simultaneously is 0.5% across all data points, and that if land type A occurs, there is a 70% probability that the oil and gas reserves are B.

[0195] Based on the aforementioned research on structured graph data models, unstructured data analysis algorithms, and correlation analysis of business attributes from spatial and temporal dimensions, an oilfield land value evaluation and analysis model is constructed. This model utilizes a data / model dual-drive hybrid simulation technology to comprehensively analyze factors such as reserve conditions, surface development difficulty, land type, land ownership, oil and gas quality, oil price trends, development history and current status, transportation costs, policies and regulations, environmental protection, and geographical location. The most suitable key influencing factors are selected, as these factors directly or indirectly determine the potential and economic benefits of oilfield development. However, considering the current state of actual oilfield data and the difficulty in quantifying some data, it is necessary to first select readily available and quantifiable influencing factors as key factors for final analysis and evaluation, calculating their support levels. This provides support for oilfield land value evaluation and analysis, and the AHP analysis method is combined to achieve oilfield land value evaluation.

[0196] Based on a comprehensive consideration of influencing factors and expert analysis, this model selects five factors as key influencing factors: land type, reserve conditions, surface development difficulty, land input, and land output.

[0197] These five categories of factors have a direct and significant impact on the development and operation of oil fields. The following is an analysis of the selection of these five categories of factors and the potential reasons for not selecting other factors:

[0198] 1) Land Categories

[0199] Land use type was chosen as a key factor because it directly determines the potential location, development cost, and feasibility of an oilfield project. Different land types have different use restrictions, acquisition difficulties, and cost levels. For example, the conversion costs and environmental impacts of agricultural land to nature reserves are completely different. Furthermore, land use type is closely linked to national land spatial planning and environmental protection regulations. Information such as land location, topography, and features is also directly related to decisions regarding engineering design, road layout, and infrastructure. Therefore, these factors are extracted as core influencing factors, enabling the construction of closely correlated analysis models with multiple business attributes, providing fundamental data for oilfield project site selection.

[0200] 2) Reserve conditions

[0201] Reserve conditions are one of the core parameters for assessing the value and development potential of an oil field. The quantity and distribution of oil and gas reserves determine the scale, extraction difficulty, and economic returns of an oil field project. Exploration and appraisal well engineering provides detailed data on reserve size and geological characteristics. Furthermore, reserve conditions are closely related to development strategies, production planning, and return on investment. This factor must be carefully considered during site selection to ensure the economic viability of the project and the efficient development of resources.

[0202] 3) Difficulty of ground development

[0203] The difficulty of surface development encompasses natural factors such as topography, soil quality, and climate, as well as social factors such as infrastructure support, transportation connectivity, and engineering construction complexity. These factors directly impact the cost and project cycle of oilfield projects. The level of surface development difficulty is closely linked to energy reserves, land use, and environmental protection. For example, complex terrain or environmentally sensitive areas will lead to higher construction costs and stricter environmental protection requirements. Furthermore, development difficulty also affects subsequent development plan design and the management of major development trials.

[0204] 4) Land investment

[0205] Land investment is a significant cost in the initial stages of an oilfield project's operation, encompassing land acquisition, compensation, leveling, and infrastructure construction. It directly impacts the project's start-up capital requirements and rate of return. The level of land investment not only affects the project's economic benefits but also determines its smooth progress and sustainable operation. For example, acquiring high-value commercial or industrial land will significantly increase land investment, but it may also bring more convenient transportation and more comprehensive supporting facilities, helping to enhance the overall competitiveness of the oilfield project.

[0206] 5) Land output

[0207] Land output directly reflects the economic benefits of an oilfield project, encompassing multiple aspects such as oil and gas production, sales revenue, and profits. It is one of the key indicators for assessing the value of oilfield land. The level of land output is not only related to natural factors such as reserve conditions and development difficulty, but also influenced by external factors such as market environment, oil price trends, and policies and regulations. Improving land output can effectively enhance the economic benefits and return on investment of oilfield projects, thereby increasing the value of oilfield land.

[0208] 4) Potential reasons for not selecting other factors

[0209] ① Degree of Impact: Although some factors, such as oil and gas quality, development history and current status, and transportation costs, have a significant impact on oilfield development and operation, they primarily affect the value of oilfield land use indirectly, or their impact is relatively small. In contrast, factors such as land type, reserve conditions, surface development difficulty, land input, and land output directly determine the potential and economic benefits of oilfield development, and are therefore selected as key influencing factors.

[0210] ② Representativeness and Quantifiability: The research objective of this project is to construct an analysis model for evaluating the value of oilfield land, aiming to assess the value of oilfield land through comprehensive analysis of various factors. Therefore, when selecting key influencing factors, it is necessary to consider the representativeness and quantifiability of these factors. Factors such as land type and reserve conditions have clear classifications and quantifiable standards, making it easier to construct evaluation models and conduct calculations and analyses.

[0211] ③ Interactions: Oilfield development is a complex process involving the interactions and influences of numerous factors. When selecting key influencing factors, it is necessary to comprehensively consider the correlations and interactions among these factors. Although the unselected factors may have some impact on oilfield development and operation, under the current research framework and conditions, they may not be the primary factors determining the value of oilfield land use, and therefore were not selected as key influencing factors.

[0212] In summary, through comprehensive analysis and comparison of various factors, this study selected land type, reserve conditions, surface development difficulty, land input, and land output as key influencing factors to construct an oilfield land value evaluation and analysis model. This selection aims to more accurately assess the value of oilfield land and provide strong decision support for oilfield development and operation.

[0213] In a specific embodiment of the present invention, step 3: determining the comprehensive evaluation coefficient of the impact factor based on the analytic hierarchy process (AHP) and expert scoring includes: quantifying the five key impact factors using indicators based on the AHP and expert scoring, thereby determining the comprehensive evaluation coefficient of the impact factor, wherein...

[0214] The following are the specific steps for quantifying the key factors affecting the evaluation and analysis of oilfield land use value using the Analytic Hierarchy Process (AHP):

[0215] Step 301: Decompose each impact factor into one or more first impact factors, and each first impact factor into multiple second impact factors to establish a hierarchical analysis model;

[0216] The hierarchical model is constructed as follows: First, the five key influencing factors are decomposed into different elements, and then arranged into a hierarchical structure according to the relationships between them. In this case, the top layer is the overall oilfield land value assessment, the middle layer (criteria layer) is the major categories of influencing factors, and the bottom layer (sub-criteria layer) is the specific minor categories of influencing factors, thus constructing a hierarchical structure:

[0217] 1) Top-level objective: To conduct a value assessment of oilfield land.

[0218] 2) Criteria Layer (Five Categories): Land Type, Reserve Conditions, Difficulty of Surface Development, Land Input and Land Output.

[0219] 3) Sub-criteria layer (48 sub-categories): Mining land, unused land, grassland, forest land, ordinary cultivated land, basic farmland, special land, areas outside mining rights, land within ordinary urban planning areas, land within the Xinjiang Production and Construction Corps boundary, land within the capital city planning areas, railway and highway facilities, core areas of nature reserves, non-core areas of nature reserves, land for water conservancy facilities such as rivers and reservoirs, land for municipal facilities (pipelines, power lines, etc.), areas with supporting facilities for oilfield development and production, and where supporting facilities are ≤50 km away; areas far from supporting facilities for oilfield development and production, and where supporting facilities are greater than 50 km but less than 100 km away; areas far from supporting facilities for oilfield development and production, and where supporting facilities are greater than 100 km but less than 200 km away; areas far from supporting facilities for oilfield development and production, and where supporting facilities are greater than 200 km away; favorable areas for reserves; marginal areas for rolling development; unexplored areas; temporary land use investment costs; long-term land use investment costs; holding costs; disposal and abandonment costs; output value and income per parcel per year.

[0220] Table 1. Evaluation Index System for Oilfield Land Use Value

[0221]

[0222]

[0223]

[0224] As shown in Table 1, the nature of the indicators is divided into quantitative and qualitative. The influencing factors shown in this table are all quantitative indicators, which are presented in numerical form and can be quantified and measured. The direction of the indicators is divided into positive and negative. The higher the positive indicator value, the more favorable it is to the development of oilfield land and the higher the land value. The higher the negative indicator value, the more unfavorable it is to the development of oilfield land and the lower the land value.

[0225] The second influencing factors corresponding to the land types listed in Table 1 include: mining land, unused land, grassland, forest land, ordinary cultivated land, basic farmland, special land, areas outside mining rights, land within ordinary urban planning areas, land within the Xinjiang Production and Construction Corps boundary, land within the planning areas of the capital city, facilities such as railways and highways, core areas of nature reserves, non-core areas of nature reserves, and water conservancy facilities such as rivers and reservoirs.

[0226] The second influencing factor corresponding to the reserve conditions includes the favorable area of ​​the reserves, the marginal area of ​​rolling development, and the unexplored area.

[0227] The second influencing factor corresponding to the difficulty of surface development includes areas with oilfield development and production support within ≤50 kilometers, areas far from oilfield development and production support within ≤50 kilometers and less than 100 kilometers, areas far from oilfield development and production support within ≤100 kilometers and less than 200 kilometers, and areas far from oilfield development and production support within ≤200 kilometers.

[0228] The second influencing factor corresponding to land investment includes temporary land investment costs, long-term land investment costs, holding costs, and disposal and disposal costs;

[0229] The second influencing factor corresponding to land output includes the output value and income per parcel per year.

[0230] Step 302: Using the Analytic Hierarchy Process (AHP), determine the weight coefficients corresponding to each second influencing factor in the AHP model. The steps are as follows:

[0231] Step 3021: In the analytic hierarchy process (AHP) model, each pair of the multiple second-influence factors corresponding to each first-influence factor is compared and scored, and a comparison matrix is ​​created.

[0232] For each subcategory within the listed major categories, a comparison matrix needs to be created. For example, for the major category "Land Use," all subcategories (mining land, unused land, grassland, etc.) are compared pairwise, and their relative importance is determined based on their influence factor weights. Importance can be represented on a scale of 1 to 5, where 1 indicates that both are equally important, and 5 indicates that one is extremely important to the other. The intermediate values ​​represent different levels of priority.

[0233] For the five major categories and 48 subcategories, create four pairwise comparison matrices:

[0234] 1) For the comparison matrix between the five categories, the size will be 5x5, as shown in Table 2.

[0235] Table 2. Comparison Matrix of Five Categories (S1 Land Type, S2 Reserve Conditions, S3 Surface Development Difficulty, S4 Land Input, S5 Land Output)

[0236] index S1 S2 S3 S4 S5 Weight S1 1 1 / 5 4 1 / 5 1 / 5 0.167 S2 5 1 3 1 / 3 1 / 3 0.142 S3 1 / 4 1 / 3 1 1 / 4 1 / 4 0.200 S4 5 3 4 1 1 0.290 S5 5 3 4 1 1 0.290

[0237] 2) For each subclass within a major category, the size will depend on the number of subclasses contained in that major category (for example, if there are 17 subclasses in the land category, the matrix size will be 17x17), as shown in Table 3-7.

[0238] Table 3. Land Category Comparison Matrix

[0239] index S1_1 S1_2 S1_3 S1_4 S1_5 S1_6 S1_7 S1_8 S1_9 S1_10 S1_11 S1_12 S1_13 S1_14 S1_15 S1_16 S1_17 Weight S1_1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 0.220 S1_2 1 / 5 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0.176 S1_3 1 / 5 1 / 4 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 0.143 S1_4 1 / 5 1 / 4 1 / 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 0.121 S1_5 1 / 5 1 / 4 1 / 3 1 / 3 1 2 2 2 2 2 2 2 2 2 2 1 / 2 2 0.106 S1_6 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 2 2 2 2 2 2 2 2 2 1 / 2 2 0.096 S1_7 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 2 2 2 2 2 2 2 2 1 / 2 2 0.089 S1_8 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 2 2 2 2 2 2 2 1 / 2 2 0.083 S1_9 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 2 2 2 2 2 2 1 / 2 2 0.077 S1_10 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 2 2 2 2 2 1 / 2 2 0.073 S1_11 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 2 2 2 2 1 / 2 2 0.069 S1_12 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 2 2 2 1 / 2 2 0.065 S1_13 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 2 2 1 / 2 2 0.061 S1_14 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 2 1 / 2 2 0.056 S1_15 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 1 / 2 2 0.052 S1_16 1 / 5 1 / 4 1 / 3 1 / 3 2 2 2 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 2 0.050 S1_17 1 / 5 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 0.045

[0240] (There are 3 subcategories of reserve conditions, and the matrix size is 3×3).

[0241] Table 4. Comparison Matrix of Reserve Conditions

[0242] index S2_1 S2_2 S2_3 Weight S2_1 1 1 / 3 1 / 4 0.174 S2_2 3 1 1 / 5 0.206 S2_3 4 5 1 0.620

[0243] Table 5. Comparison Matrix of Ground Development Difficulty

[0244] index S3_1 S3_2 S3_3 S3_4 Weight S3_1 1 3 4 5 0.517 S3_2 1 / 3 1 3 4 0.272 S3_3 1 / 4 1 / 3 1 1 0.101 S3_4 1 / 5 1 / 4 1 1 0.090

[0245] Table 6. Land Input Comparison Matrix

[0246]

[0247]

[0248] Table 7. Land Output Comparison Matrix

[0249] index S5_1 S5_2 S5_3 S5_4 S5_5 S5_6 S5_7 S5_8 S5_9 S5_10 Weight S5_1 1 2 3 4 3 2 2 3 4 2 0.084 S5_2 1 / 2 1 2 3 2 1 2 2 3 1 0.051 S5_3 1 / 3 1 / 2 1 2 2 1 2 2 3 1 0.049 S5_4 1 / 4 1 / 3 1 / 2 1 2 1 / 2 1 2 2 1 / 2 0.043 S5_5 1 / 3 1 / 2 1 / 2 1 / 2 1 1 / 2 1 1 2 1 / 2 0.042 S5_6 1 / 2 1 1 2 2 1 1 2 2 1 0.051 S5_7 1 / 2 1 / 2 1 / 2 1 1 1 1 2 2 1 0.050 S5_8 1 / 3 1 / 2 1 / 2 1 / 2 1 1 / 2 1 / 2 1 2 1 / 2 0.046 S5_9 1 / 4 1 / 3 1 / 3 1 / 2 1 / 2 1 / 2 1 / 2 1 / 2 1 1 / 2 0.036 S5_10 1 / 2 1 1 2 2 1 1 2 2 1 0.049

[0250] Step 3022: Use the consistency index and random consistency ratio to test the consistency of the comparison matrix, and calculate the formulas for CI, RI, and CR; where,

[0251] Comparison Matrix and Consistency Check: A comparison matrix is ​​created by comparing and scoring elements pairwise at each level. For example, within the "Land Type" category, each subcategory is compared pairwise by importance. Then, the consistency of the comparison matrix is ​​checked using the AHP consistency index CI and the random consistency ratio CR to ensure that the comparisons provided are consistent (CR is typically less than 0.1).

[0252] If the matrix consistency comparison fails, meaning the calculated CR value exceeds the usual acceptance criterion (0.1), it indicates insufficient matrix consistency, and the pairwise comparison criteria need to be re-examined and re-evaluated.

[0253] Reassess the judgment: Check all pairwise comparisons for logical errors or evaluation biases, especially those with high weighting ratios.

[0254] Step 303: Use expert scoring to determine the weight coefficients corresponding to each second influencing factor in the analytic hierarchy process (AHP) model;

[0255] The expert scoring method is also a qualitative description and quantitative method. The main steps of the expert scoring method are: first, select evaluation indicators according to the specific situation of the evaluation object, and determine the evaluation level for each indicator. The standard of each level is expressed by a score. Then, based on this, experts analyze and evaluate the evaluation object, determine the score of each indicator, and use the addition scoring method, multiplication scoring method or addition-multiplication scoring method to calculate the total score of each evaluation object, thereby obtaining the evaluation result.

[0256] The expert scoring criteria: Oilfield well location selection must first adhere to the principle of "surface yielding to underground, and underground taking care of surface," while comprehensively considering factors such as topography, land type, transportation, safety, and environment. According to industry standards, when determining well locations, construction units should conduct surveys and investigations of residential areas, schools, factories and mines (including mining units extracting underground resources), national defense facilities, high-voltage power lines, highways (national and provincial), railways, water resources, and wind direction changes within a 3km radius of exploratory wells, a 2km radius of new development wells, and a 1km radius of other wells. They should also survey the underground pipeline network (oil pipelines, water pipelines, gas pipelines, and cables, etc.) and communication lines within the well site coverage area.

[0257] In a specific embodiment of the present invention, for the well site selection of a certain oilfield, the following table 8 shows the inspection standards for well site selection in a certain oilfield:

[0258] Table 8 Inspection Standards for Well Site Selection in a Certain Oilfield

[0259]

[0260]

[0261] According to Table 8, the following information is provided for well site selection in a certain oilfield:

[0262] 1) The distance between the wellhead and high-voltage lines and other permanent facilities shall not be less than 75m;

[0263] 2) The distance from residential buildings shall not be less than 100m;

[0264] 3) The distance to railways and highways shall not be less than 200m;

[0265] 4) The distance from schools, hospitals, oil depots, rivers, reservoirs (for wells with a depth greater than 800m, the distance from the reservoir dam should be no less than 1200m), densely populated buildings (buildings in public activity places where more than 50 people gather at the same time), and high-risk places should be no less than 500m.

[0266] 5) The distance between wellheads of oil and gas wells shall not be less than 2m. The distance between the wellhead of high-pressure, high-sulfur oil and gas wells and the wellhead of other wells shall be greater than the length of the drilling platform of the drilling rig used to drill this well, and not less than 8m.

[0267] 6) When drilling in underground mineral mining areas, the distance between the wellbore and the mining tunnel or mine shaft shall not be less than 100m, and the casing depth shall seal the mining layer and exceed the bottom boundary of the mining section by 100m.

[0268] The relative importance of each influencing factor is determined based on industry standards and expert opinions. Then, pairwise comparisons are performed on each factor, and a relative importance score is assigned. Simultaneously, based on the expert scores, the weight coefficient for each influencing factor is calculated. This is typically achieved using a normalized comparison matrix, detailed in Tables 2-7 of step 3021 above.

[0269] Step 3031: In the hierarchical analysis model, the multiple second-influence factors corresponding to each first-influence factor are compared pairwise, and a comparison matrix is ​​created based on the expert's judgment score;

[0270] Aggregate expert judgments: If there are multiple expert decision-makers, the weights given by different decision-makers need to be reasonably aggregated to obtain a collectively consistent decision result.

[0271] Consulting experts: Invite more experts to conduct the evaluation to increase the diversity and accuracy of the assessment.

[0272] Use the median or mean: Using the median or mean as determined by experts can improve the consistency of the matrix.

[0273] Weight Calculation and Ranking: By normalizing the comparison matrix and calculating its eigenvectors, the relative weights of each influencing factor (such as criteria and sub-criteria) can be obtained. These weights are normalized to ensure a sum of 1, thus reflecting the relative importance of each factor in the decision-making process. This method helps decision-makers clarify the contribution of different factors to the overall objective (such as location decisions), making the entire decision-making process both scientific and systematic. These weights are then ranked to determine the degree of contribution of each influencing factor to the overall decision.

[0274] Consistency Index (CI):

[0275]

[0276] λmax is the largest eigenvalue of the pairwise comparison matrix.

[0277] n is the number of criteria or sub-criteria.

[0278] Furthermore, regarding the random consistency index (RI), RI is an average random consistency metric given by the matrix order. It can be found in Table 9:

[0279] Table 9. Comparison Matrix RI Table

[0280]

[0281]

[0282] The calculation of the Random Consistency Index (RI) is based on statistical methods and the characteristics of randomly generated matrices. Table 9 provides a table of commonly used RI indices, derived through statistical analysis of a large number of randomly generated matrices and their consistency indices. RI values ​​are used to provide a reference for testing the consistency of actual comparison matrices. By comparing the consistency indices of actual comparison matrices with the RI values, the reasonableness of the matrix's consistency can be determined.

[0283] Consistency Ratio (CR)

[0284] [CR={CI} / {RI}]

[0285] If (CR < 0.10), the matrix is ​​considered to have an acceptable level of consistency.

[0286] Calculate the CR for each pairwise comparison matrix. If the CR is less than 0.10, it is acceptable; otherwise, re-evaluate the relative importance of the criteria or sub-criteria.

[0287] ① Based on the land category comparison matrix of index S1, where λ_S1{max}=19.194, n=4, RI=1.61,

[0288] Calculate CI = (19.194-17) / (17-1) = 0.137125;

[0289] Calculate CR = 0.137125 / 1.61 ≈ 0.0852;

[0290] Therefore, a CR of less than 0.1 is acceptable.

[0291] ② According to the S2 reserve condition matrix, where λ_S2{max}=3.133, n=3, RI=0.58,

[0292] Calculate CI = (3.113-3) / (3-1) = 0.05;

[0293] Calculate CR = 0.05 / 0.58 ≈ 0.097;

[0294] Therefore, a CR of less than 0.1 is acceptable.

[0295] ③ Based on the S3 ground development difficulty matrix, where λ_S3{max}=4.098, n=4, RI=0.90,

[0296] Calculate CI = (4.098-4) / (4-1)≈0.033;

[0297] Calculate CR = 0.033 / 0.90 ≈ 0.037;

[0298] Therefore, a CR of less than 0.1 is acceptable.

[0299] ④ According to the land input matrix of indicator S4, where λ_S4{max}=14.818, n=4, RI=1.57,

[0300] Calculate CI = (14.818-14) / (14-1)≈0.06299;

[0301] Calculate CR = 0.06299 / 1.57 ≈ 0.04012;

[0302] Therefore, a CR of less than 0.1 is acceptable.

[0303] ⑤ According to the land output matrix of index S5, where λ_S4{max}=10.291, n=4, RI=1.49,

[0304] Calculate CI = (10.291-10) / (4-1)≈0.0324;

[0305] Calculate CR = 0.0324 / 1.49 ≈ 0.0217;

[0306] Therefore, a CR of less than 0.1 is acceptable.

[0307] Step 204: Fill in the score and calculate the weight vector

[0308] Complete the AHP tables (see Tables 3, 4, 5, 6, and 7) based on expert judgment. The scores for each table are S3_n in Table 3, S4_n in Table 4, S5_n in Table 5, S6_n in Table 6, and S7_n in Table 7, ranging from 1 to 5. This range is based on human intuition regarding the relative importance of things. When two factors are almost equally important, the score may be close to 1; while when one factor is extremely important relative to the other, the score may be close to 5. For each pairwise comparison matrix, normalization is performed using the same method, the largest eigenvalue of the pairwise comparison matrix is ​​calculated, and the weight vector and CI value are determined.

[0309] Calculate the overall weight of the top-level objective: Multiply the weights of the lower-level objectives by the weights of their corresponding upper-level objectives, and sum them up to obtain the overall weight for the top-level objective, as detailed in the table below. Overall Weight = Σ(Category Weight × Lower-Level Weight):

[0310] The primary and secondary weights in Table 10 are obtained from step 2021. Specifically, the primary weights are obtained from the weights calculated in the comparison matrix in Table 2, i.e., S1 weight is 0.167, S2 weight is 0.142, S3 weight is 0.2, S4 weight is 0.29, and S5 weight is 0.29. The secondary weights are calculated from the weights calculated in the land type comparison matrix in Table 3, the reserve condition comparison matrix in Table 4, the surface development difficulty comparison matrix in Table 5, the land input comparison matrix in Table 6, and the land output comparison matrix in Table 7.

[0311] Table 10. Comparison Matrix Weight Calculation Table

[0312]

[0313]

[0314]

[0315] Step 205: Perform a weighted calculation on the weight coefficients determined by the analytic hierarchy process (AHP) and the expert scoring method to determine the comprehensive evaluation coefficient for each influencing factor.

[0316]

[0317]

[0318]

[0319]

[0320]

[0321] In the formula, Q 土地地类 Q represents the comprehensive evaluation coefficient of land use type; 储量条件 Q represents the comprehensive evaluation coefficient of factors affecting reserves conditions; 地面开发难度 Q represents the comprehensive evaluation coefficient indicating the factors influencing the difficulty of ground development; 土地投入 Q represents the comprehensive evaluation coefficient of land input influencing factors; 土地产出 The comprehensive evaluation coefficient representing the factors influencing land output, x 1i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in land use classification; y 1i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in the land category; x 2i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in the reserve conditions; y 2i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in the reserve conditions; x 3iThis represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process (AHP) in the difficulty of ground development; y 3i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in the difficulty of ground development; x 4i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in land input; y 4i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in land investment; x 5i y represents the weight coefficient corresponding to the i-th second influencing factor determined by the analytic hierarchy process in land output. 5i This represents the weight coefficient corresponding to the i-th second influencing factor determined by the expert scoring method in land output.

[0322] In a specific embodiment of the present invention, step 4: analyzing the value of oilfield land based on the comprehensive evaluation coefficient includes: assessing the value of the land based on the obtained weight results. After the weight of each factor in the land value evaluation coefficient generation process is determined, the land value evaluation ratio (comprehensive evaluation value) can be calculated using the following formula:

[0323]

[0324] in,

[0325] Input evaluation value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0326] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%

[0327] In the formula, Q 土地地类 Q represents the comprehensive weighting coefficient of land categories; 储量条件 Q represents the comprehensive weighting coefficient of factors influencing reserves conditions; 地面开发难度子 Q represents the comprehensive weighting coefficient of factors influencing the difficulty of ground development; 土地投入 Q represents the comprehensive weighting coefficient of land input influencing factors; 土地产出 This represents the comprehensive weighting coefficient of factors influencing land output.

[0328] The weights were assigned using the comparison matrix weight calculation table in Table 10. Using the AHP method, quantitative indicators for the following five categories of factors were established, as shown in Tables 11-1, 11-2, 11-3, 11-4, 11-5, and 11-6.

[0329] Table 11-1. Quantitative Indicators of Land Use Type Influencing Factors

[0330]

[0331]

[0332] Table 11-2. Quantitative Indicators of Factors Affecting Reserve Conditions

[0333]

[0334] Table 11-3. Quantitative Indicators of Factors Affecting Ground Development Difficulty

[0335]

[0336]

[0337] Table 11-4. Quantitative Indicators of Land Input Influencing Factors

[0338]

[0339] Table 11-5. Quantitative Indicators of Land Output Influencing Factors

[0340]

[0341]

[0342] Table 11-6. Explanation of Quantitative Indicators for Land Input-Output Influencing Factors

[0343]

[0344] Construct a multi-factor quantitative algorithm for oilfield land value evaluation;

[0345] The algorithm for assessing the value of oilfield land requires data reflecting the actual measurements of the aforementioned influencing factors, as well as their historical results. This data is used to predict and evaluate the value of oilfield land. The following is a basic framework for the algorithm:

[0346] The output ratio can be defined as the ratio of the output evaluation value to the input evaluation value, and can be expressed by the following formula:

[0347] If output value (ten thousand yuan) - input value (ten thousand yuan) ≤ 0;

[0348] Then it is directly determined to be land with negative effects;

[0349] Else.

[0350] Set the LVR (Land Value Ratio) as: (Input Value) / (Output Value)

[0351] in,

[0352] Input evaluation value = Q土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0353] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%.

[0354] Land Value Ratio (LVR) Definition: The LVR threshold is categorized into low, medium, and high as follows: High-efficiency land (≥5), Average-efficiency land (≥3, <5), Inefficient land (≥1, <3), and Negative-efficiency land (<1). A higher value indicates high-efficiency land, a lower value indicates low-efficiency land, and a negative value indicates negative-efficiency land.

[0355] ① High-efficiency land (LVR≥5):

[0356] This indicates that the land is used very efficiently, with a highly advantageous input-output ratio, where output far exceeds input. Such land offers high returns and high productivity, making it the optimal choice.

[0357] ②Land with constant availability (3≤LVR<5):

[0358] The input and output of land are relatively close. Although there is no obvious advantage in output, it can maintain a relatively stable return on production and investment.

[0359] ③ Inefficient land (1≤LVR<3):

[0360] The output of land is lower than its input, but the gap is not too large. Land use efficiency can be improved by changing the way it is used and increasing the efficiency of input.

[0361] ④ Land with negative performance (LVR<1):

[0362] Land whose output cannot compensate for its input, or even has a negative effect, should be considered substandard land. It is recommended that development be halted, or that a thorough reassessment and rezoning be conducted.

[0363] Development and application of oilfield land value assessment function

[0364] Based on a multi-factor quantitative algorithm for oilfield land value assessment, this work developed an oilfield land value assessment function. This function utilizes Web development technology to achieve online calculation and result display of oilfield land value assessment.

[0365] Users can input relevant data and parameters into the system, which will automatically calculate the value assessment results of the oilfield land and display them to the user in the form of charts and graphs. The system also provides functions such as data import / export and historical data query, facilitating data management and analysis for users.

[0366] Experiments have verified that by constructing a land use value evaluation and analysis model and comprehensively considering various influencing factors, the analysis of oilfield land use value has been successfully achieved. Experimental results show that this method demonstrates excellent application performance in terms of accurate prediction, factor importance analysis, and land use value evaluation, providing a solid guarantee for oilfield land use management.

[0367] Future research can further explore the application of machine learning algorithms in oilfield land value assessment and analysis to improve assessment accuracy and model stability. Simultaneously, a deeper analysis of influencing factors will be conducted to identify practically significant factors and optimize the assessment and analysis model. Furthermore, combining specific project case studies, the practical application strategies of multi-factor quantification algorithms in oilfield land value assessment will be investigated. By acquiring and analyzing oilfield land value data in real time, the investment potential and risks of different plots can be more accurately assessed, leading to more informed decision-making. Integration with other oilfield management systems can also be achieved to realize data sharing and collaborative work. Conducting oilfield land value assessment, production planning, and resource allocation on a unified platform will improve work efficiency and management level.

[0368] This invention also provides an apparatus for analyzing the value of oilfield land, such as... Figure 2 As shown, the device includes,

[0369] The building unit is used to construct a knowledge graph network that reflects the influencing factors of oilfield land value based on oil and gas reserve data, oil and gas production data, land type, land ownership and geospatial information.

[0370] The extraction unit is used to extract the influencing factors of oilfield land value analysis based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area. The influencing factors include land type, reserve conditions, surface development difficulty, land input and land output.

[0371] A unit is defined to determine the comprehensive evaluation coefficient of the influencing factors based on the analytic hierarchy process and expert scoring.

[0372] The analysis unit is used to analyze the value of oilfield land based on the comprehensive evaluation coefficient. Regarding the apparatus in the above embodiments, the specific manner in which each unit performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0373] Based on the above disclosure, the present invention also provides an electronic device. For example... Figure 3As shown, the electronic device of this disclosure includes at least one processor electrically connected to the present invention and at least one memory electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps as executed by the controller above.

[0374] An embodiment of the present invention also provides a storable medium storing computer instructions, which, when executed by a processor, are specifically executed according to the steps in the method described in the above embodiment.

[0375] An embodiment of the present invention also provides a computer program product, including computer instructions, which, when executed by a processor, specifically follow the steps in the method described in the above embodiment.

[0376] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0377] The multi-factor quantification algorithm proposed in this study was verified.

[0378] Example parameters: Taking a certain oilfield construction project and a certain land parcel as sample data, the specific parameters are as follows:

[0379] ① Land type: Shrubland, accounting for 80% of the area; Grassland, accounting for 20% of the area.

[0380] ② Reserve conditions: Areas with favorable reserves.

[0381] ③ Difficulty of surface development: Areas with supporting facilities for oilfield development and production, and the supporting facilities are ≤50 kilometers long.

[0382] ④ Land investment: Temporary land use: RMB 908,988; Long-term land use: RMB 418,743.74; Urban land use tax (holding cost): RMB 505,918.08.

[0383] ⑤ Land output: Annual output of 3,285 tons, with an output value of approximately 13 million yuan;

[0384] ⑥ Land revenue: 0 yuan.

[0385] According to the formula: LVR (Land Value Ratio) = (Input Value) / (Output Value); where,

[0386] Input evaluation value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0387] Output evaluation value = Q 储量条件×14.2%+Q 土地产出 ×29%

[0388] Based on the above parameters, the scores for each influencing factor can be calculated:

[0389] ① Calculation of land category weights

[0390] Shrubland: weight 0.020;

[0391] Grassland: weight 0.024;

[0392] Calculate the land category weights (weighted average):

[0393] Land category weight = (80% × 0.020) + (20% × 0.024) = 0.016 + 0.0048 = 0.0208;

[0394] ②Reserve weighting

[0395] The area has favorable reserve conditions, and the reserve weight is 0.025.

[0396] ③ Weighting of Ground Development Difficulty

[0397] The difficulty of surface development is defined as "areas with supporting oilfield development and production facilities, and the supporting facilities are ≤50 kilometers away", with a weight of 0.103 for the difficulty of surface development.

[0398] ④ Calculation of land input weight

[0399] Land investment includes temporary land use fees, long-term land use fees, and urban land use tax (holding costs):

[0400] - Temporary land use fee: RMB 908,988 (≥ RMB 300,000, weight 0.026);

[0401] -Long-term land use fee: RMB 418,743.74 (≥ RMB 50,000, weight 0.026);

[0402] -Urban land use tax: 505,918.08 yuan (≥5000 yuan, weight 0.021);

[0403] The total input weight is: land input weight = 0.026 + 0.026 + 0.021 = 0.073;

[0404] ⑤ Output weight calculation

[0405] The annual output is 3,285 tons, with an output value of approximately 13 million yuan, corresponding to an output value weight of 0.012 (1000≤output value<2000 million yuan).

[0406] ⑥ Result Calculation

[0407] According to the LVR (Land Value Ratio) land valuation ratio formula:

[0408] LVR (Land Value Ratio) = (Input Value) / (Output Value); where, Input Value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0409] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%.

[0410] Substitute the following weight values: Land type weight = 0.0208, Ground development difficulty weight = 0.103, Land input weight = 0.073, Reserve weight = 0.025, Output weight = 0.012.

[0411] The final calculated LVR is 0.045244 / 0.00703 ≈ 6.44.

[0412] ⑦ Results Analysis

[0413] According to the definition of land value assessment ratio:

[0414] - High-efficiency land (LVR≥5);

[0415] -Land with stable value (3≤LVR<5);

[0416] - Inefficient land (1≤LVR<3);

[0417] -Land with negative performance (LVR<1);

[0418] LVR = 6.44, which means that the land is used very efficiently and is considered high-efficiency land.

[0419] Therefore, the land's valuation ratio is 6.44, indicating very high land use efficiency and a highly advantageous input-output ratio, with output far exceeding input. This type of land offers high returns and high productivity, making it the optimal choice.

[0420] The method of the present invention can achieve the following effects:

[0421] 1) Improved Efficiency of Oilfield Land Valuation: The application of this innovative achievement has significantly improved the efficiency of oilfield land valuation. Traditional assessment methods typically require substantial time and manpower, while this achievement automates the assessment process, significantly shortening the assessment cycle. Preliminary statistics indicate that project assessment time has been reduced by an average of 15-30 days, and assessment costs have been lowered by 10%-20%.

[0422] 2) Improved assessment accuracy: This work employs a multi-source data fusion approach, fully considering five major influencing factors: land type, land reserves, surface development difficulty, land input, and land output. Quantification and weighting were performed using the analytic hierarchy process (AHP) and expert scoring. This comprehensive assessment method makes the results more accurate and reliable.

[0423] 3) Reduced labor costs: The automated evaluation process of this innovation reduces reliance on manual labor and reduces a lot of tedious data collection and processing work, thereby reducing additional costs caused by decision delays.

[0424] 4) Enhance decision support capabilities: Through visualization analysis tools, decision-makers can more intuitively understand the value distribution and influencing factors of oilfield land, and can more clearly understand the value differences of different plots in the oilfield, providing new ideas and methods for oilfield land management.

[0425] 5) Optimize resource allocation: By applying this achievement, the value of different plots in an oilfield can be assessed more accurately, thereby optimizing resource allocation based on strategic needs and development plans, and maximizing resource utilization.

[0426] 6) Improve land use efficiency: Accurate land value assessment can help oil fields to conduct land use planning more scientifically, improve land use efficiency, and reduce land waste.

[0427] 7) Significant Impact on Strategic and Land Resource Planning and Utilization in Oilfields: The successful development and application of this achievement not only improves the efficiency and accuracy of oilfield land value assessment but also lays a solid foundation for the development of oilfields in strategic and land resource planning and utilization. Through continuous technological innovation and research and development, it can maintain a leading position in fierce market competition and provide strong guarantees for future sustainable development. The entire implementation process of this invention is introduced using the five categories of quantitative indicators of influencing factors in the Analytic Hierarchy Process (AHP) as an example.

[0428] (1) Quantitative Indicators

[0429] The evaluation indicators for oilfield land use value include five major categories of factors: land type, reserve conditions, surface development difficulty, land input, and land output. Each major category of factors is further divided into one or two levels of indicators (see Tables 11-1 to 11-6).

[0430] (2) Calculation of oilfield land value

[0431] The method for evaluating the value of oilfield land is used to calculate the input-output ratio of oilfield land, as detailed in the following formula:

[0432] If output value (ten thousand yuan) - input value (ten thousand yuan) ≤ 0;

[0433] Then it is directly determined to be land with negative effects;

[0434] Else.

[0435] LVR (Land Value Ratio) = (Input Value) / (Output Value); where, Input Value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0436] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%.

[0437] Land Value Ratio (LVR) Definition: The LVR threshold is divided into high-efficiency land (≥1.0), medium-efficiency land (≥0.5, <1.0), low-efficiency land (≥0.2, <0.5), and negative-efficiency land (<0.2). The higher the value, the more efficient the land is; the lower the value, the less efficient the land is; and a negative value indicates negative-efficiency land.

[0438] ① High-efficiency land (LVR≥5):

[0439] This indicates that the land is used very efficiently, with a highly advantageous input-output ratio, where output far exceeds input. Such land offers high returns and high productivity, making it the optimal choice.

[0440] ②Land with constant availability (3≤LVR<5):

[0441] The input and output of land are relatively close. Although there is no obvious advantage in output, it can maintain a relatively stable return on production and investment.

[0442] ③ Inefficient land (1≤LVR<3):

[0443] The output of land is lower than its input, but the gap is not too large. Land use efficiency can be improved by changing the way it is used and increasing the efficiency of input.

[0444] ④ Land with negative performance (LVR<1):

[0445] Land whose output cannot compensate for its input, or even has a negative effect, should be considered substandard land. It is recommended that development be halted, or that a thorough reassessment and rezoning be conducted.

[0446] Example 1:

[0447] Taking land parcel "1540" from a certain oilfield construction project as sample data, the specific parameters are as follows:

[0448] Land type: Unused land, accounting for 100% of the area;

[0449] Reserve conditions: Areas with favorable reserves;

[0450] Surface development difficulty: The area is far from the supporting facilities for oilfield development and production, and the supporting facilities are more than 50 km but less than 100 km away;

[0451] Land investment: Holding cost: 3400 yuan;

[0452] Land output: Annual output of 12 tons, with a value of approximately 43,200 yuan;

[0453] According to the formula:

[0454] LVR (Land Value Ratio) = (Input Value) / (Output Value); where, Input Value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0455] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%.

[0456] ① Calculation of land category weights

[0457] The land type is unused land, accounting for 100% of the area, so the land type weight is 0.029.

[0458] ②Reserve weighting

[0459] The area has favorable reserve conditions, and the reserve weight is 0.025.

[0460] ③ Weighting of Ground Development Difficulty

[0461] The difficulty of surface development is defined as "areas far from the supporting facilities for oilfield development and production, with supporting facilities located more than 50 kilometers but less than 100 kilometers away", and the weight of the difficulty of surface development is 0.089.

[0462] ④ Calculation of land input weight

[0463] Land investment includes holding costs of 3,400 yuan, with a corresponding urban land use tax weight of 0.019.

[0464] ⑤ Output weight calculation

[0465] Annual output is 12 tons, with a value of approximately 43,200 yuan. Since the output value is less than 1 million yuan, the corresponding output value weight is 0.024.

[0466] ⑥ Result Calculation

[0467] According to the formula: LVR (Land Value Ratio) = (Input Value) / (Output Value);

[0468] in,

[0469] Input evaluation value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0470] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%;

[0471] Substitute the following weight values: Land type weight = 0.029, Ground development difficulty weight = 0.089, Ground development difficulty weight = 0.089, Land input weight = 0.019, Land input weight = 0.019, Reserve weight = 0.025, Reserve weight = 0.025, Output weight = 0.024;

[0472] The final calculated LVR is 0.028153 / 0.001051 ≈ 2.68;

[0473] ⑦ Results Analysis

[0474] According to the definition of land value assessment ratio:

[0475] - High-efficiency land (LVR≥5);

[0476] -Land with stable value (3≤LVR<5);

[0477] - Inefficient land (1≤LVR<3);

[0478] -Land with negative performance (LVR<1);

[0479] LVR = 2.68, which means that the land is used very efficiently and is considered inefficient land.

[0480] Conclusion: The land's value ratio is 2.68, indicating that the land's output is lower than its input, but the gap is not too large. Land use efficiency can be improved by modifying land use methods and increasing input efficiency.

[0481] Example 2:

[0482] Taking land parcel "1108" from a certain oilfield construction project as sample data, the specific parameters are as follows:

[0483] Land type: Mining land, accounting for 100% of the area, with a suitability rating of 100%;

[0484] Reserve conditions: Areas with favorable reserves;

[0485] Surface development difficulty: Areas with oilfield development and production support facilities, and the support facilities are ≤50 kilometers away;

[0486] Land investment: Holding cost: 3400 yuan;

[0487] Land output: Annual output of 0 tons, with an output value of approximately 0 yuan;

[0488] The output ratio can be defined as the ratio of the output evaluation value to the input evaluation value, and can be expressed by the following formula:

[0489] If output value (ten thousand yuan) - input value (ten thousand yuan) ≤ 0;

[0490] Then it is directly determined to be land with negative effects;

[0491] Else;

[0492] LVR (Land Value Ratio) = (Input Value) / (Output Value); where, Input Value = Q 土地地类 ×16.7%+Q 地面开发难度 ×20%+Q 土地投入 ×29%

[0493] Output evaluation value = Q 储量条件 ×14.2%+Q 土地产出 ×29%.

[0494] Since the land output value is 0 and the input evaluation value is 0.034 million yuan, the output evaluation value minus the input evaluation value is less than or equal to 0. Therefore, the land is considered ineffective land.

[0495] Conclusion: It is recommended to seal the wells and withdraw from the project to reduce overall land costs.

[0496] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of oilfield value analysis, characterized by, The method comprises, Based on oil and gas reserves data, oil and gas production data, land use type, land ownership and geographic information, a knowledge graph network reflecting the influencing factors of oilfield land value is constructed; Based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area, the influencing factors of oilfield land value analysis are extracted, including land use type, reserve condition, ground development difficulty, land input and land output; Based on the analytic hierarchy process and expert scoring, the comprehensive evaluation coefficient of the influencing factors is determined; Based on the comprehensive evaluation coefficient, the oilfield land value is analyzed.

2. The method for analyzing the value of oilfield land according to claim 1, wherein The actual oilfield land evaluation data of the target area includes target oilfield reserve data, target oil and gas production data, target ground engineering data, target land ownership data, target well location data, target land business data and target land use type.

3. The method for oilfield value-in-use analysis of claim 2, wherein, Based on the knowledge graph network reflecting the influencing factors of oilfield land value and the actual oilfield land evaluation data of the target area, the influencing factors of oilfield land value analysis are extracted, including: Using natural language processing technology, key entities, relationships and attributes are extracted from the actual oilfield land evaluation data of the target area to construct an oilfield land value knowledge base; Based on the knowledge graph network reflecting the influencing factors of oilfield land value, the oilfield land value knowledge base is analyzed using the knowledge graph network reflecting the influencing factors of oilfield land value to extract the influencing factors of oilfield land value analysis.

4. The method for analyzing the value of oilfield land according to claim 1, wherein, Based on the analytic hierarchy process and expert scoring, the comprehensive evaluation coefficient of the influencing factors is determined, including: Setting the influencing factors of oilfield land value analysis as first influencing factors; Each first influencing factor is decomposed into a plurality of second influencing factors to establish an analytic hierarchy model; In the analytic hierarchy model, the top target is to evaluate the value of oilfield land; the criterion layer includes the first influencing factors, and the sub-criterion layer includes the second influencing factors; Using the analytic hierarchy process, the weight coefficient corresponding to each second influencing factor in the analytic hierarchy model is determined; Using the expert scoring method, the weight coefficient corresponding to each second influencing factor in the analytic hierarchy model is determined; The weight coefficients determined by the analytic hierarchy process and the weight coefficients determined by the expert scoring method are weighted and calculated to determine the comprehensive evaluation coefficient of each influencing factor.

5. The method for oilfield value-in-use analysis of claim 4, wherein, Using the analytic hierarchy process, the weight coefficient corresponding to each second influencing factor in the analytic hierarchy model is determined, including: In the analytic hierarchy model, the plurality of second influencing factors corresponding to each first influencing factor are compared and scored two by two to create a comparison matrix; The consistency of the comparison matrix is tested using a consistency index and a random consistency ratio; After testing the consistency, the comparison matrix is normalized and the characteristic vector is calculated to determine the weight coefficient corresponding to each second influencing factor.

6. The method for analyzing the value of oilfield land according to claim 4, wherein, Using the expert scoring method, the weight coefficient corresponding to each second influencing factor in the analytic hierarchy model is determined, including: In the analytic hierarchy process model, the multiple second influence factors corresponding to each first influence factor are compared with each other, and a comparison matrix is created according to the expert judgment score; Each comparison matrix is normalized and a characteristic vector is calculated to determine a weight coefficient; The consistency of the normalized comparison matrix is verified by using a consistency index and a random consistency ratio, and when the random consistency ratio is less than 0.10, the weight coefficient is the weight coefficient corresponding to each second influence factor.

7. The method for analyzing the value of oilfield land use according to any one of claims 3-6, characterized in that, the second influence factors corresponding to the land types include mining land, unused land, grassland, forest land, ordinary farmland, basic farmland, special land, areas other than mining rights, land within ordinary urban planning areas, land within the boundaries of military units, land within the capital city planning area, land within the railway and highway facilities, core areas of nature reserves, non-core areas of nature reserves, and water conservancy facilities such as river and reservoirs; or / and, the second influence factors corresponding to the reserve conditions include favorable areas of reserves, marginal areas of rolling development, and unproven areas; or / and, the second influence factors corresponding to the ground development difficulty include areas with oilfield development and production support within 50 kilometers, areas far from oilfield development and production support within 50-100 kilometers, areas far from oilfield development and production support within 100-200 kilometers, and areas far from oilfield development and production support more than 200 kilometers; or / and, the second influence factors corresponding to land investment include temporary land investment costs, long-term land investment costs, holding costs, and disposal and abandonment costs; or / and, the second influence factors corresponding to land output include land value and land income.

8. The method for oilfield value-in-use analysis of claim 7, wherein, The weight coefficients determined by the analytic hierarchy process and the weight coefficients determined by the expert scoring method are weighted and calculated to determine the comprehensive weight coefficients corresponding to each influence factor, wherein In the formula, Q 土地地类 represents the comprehensive weight coefficient of the land type; Q 储量条件 represents the comprehensive weight coefficient of the reserve condition impact factor; Q 地面开发难度 represents the comprehensive weight coefficient of the ground development difficulty impact factor; Q 土地投入 represents the comprehensive weight coefficient of the land input impact factor; Q 土地产出 represents the comprehensive weight coefficient of the land output impact factor, x 1i represents the weight coefficient corresponding to the i th second impact factor in the land type determined by the analytic hierarchy process; y 1i represents the weight coefficient corresponding to the i th second impact factor in the land type determined by the expert scoring method; x 2i represents the weight coefficient corresponding to the i th second impact factor in the reserve condition determined by the analytic hierarchy process; y 2i represents the weight coefficient corresponding to the i th second impact factor in the reserve condition determined by the expert scoring method; x 3i represents the weight coefficient corresponding to the i th second impact factor in the ground development difficulty determined by the analytic hierarchy process; y 3i represents the weight coefficient corresponding to the i th second impact factor in the ground development difficulty determined by the expert scoring method; x 4i represents the weight coefficient corresponding to the i th second impact factor in the land input determined by the analytic hierarchy process; y 4i represents the weight coefficient corresponding to the i th second impact factor in the land input determined by the expert scoring method; x 5i represents the weight coefficient corresponding to the i th second impact factor in the land output determined by the analytic hierarchy process, y 5i represents the weight coefficient corresponding to the i th second impact factor in the land output determined by the expert scoring method.

9. The method for oilfield value-in-use analysis of claim 8, wherein, the comprehensive weight coefficients are used to analyze the value of oilfield land use, including: the sum of the input evaluation values of land types, ground development difficulty, and land investment is the oilfield land use input evaluation value; the sum of the output evaluation values of reserve conditions and land output is the oilfield land use output evaluation value; the ratio of the oilfield land use input evaluation value to the oilfield land use output evaluation value is the land value evaluation ratio; based on the land value evaluation ratio, the value of oilfield land use is determined to be high-efficiency land, medium-efficiency land, low-efficiency land, or negative-efficiency land.

10. The method for oilfield value-in-use analysis of claim 9, wherein, The calculation formula of the land value evaluation ratio is as follows: wherein, Investment evaluation value = Q 土地地类 x 16.7% + Q 地面开发难度 x 20% + Q 土地投入 x 29% Output evaluation value = Q 储量条件 x 14.2% + Q 土地产出 x 29% In the formula, Q 土地地类 represents the comprehensive weight coefficient of the land type; Q 储量条件 represents the comprehensive weight coefficient of the reserve condition influence factor; Q 地面开发难度 represents the comprehensive weight coefficient of the ground development difficulty influence factor; Q 土地投入 represents the comprehensive weight coefficient of the land input influence factor; Q 土地产出 represents the comprehensive weight coefficient of the land output influence factor.

11. The method for analyzing the value of oilfield land use according to claim 9, characterized in that, when the land value evaluation ratio is greater than or equal to 5, the oilfield land use is high-efficiency land; when the land value evaluation ratio is greater than or equal to 3 and less than 5, the oilfield land use is medium-efficiency land; when the land value evaluation ratio is greater than or equal to 1 and less than 3, the oilfield land use is low-efficiency land; when the land value evaluation ratio is less than 1, the oilfield land use is negative-efficiency land.

12. An apparatus for oilfield value analysis, characterized by, The device includes, The construction unit is configured to construct a knowledge graph network reflecting the value influencing factors of the oilfield land based on oil and gas reserves data, oil and gas production data, land types, land ownership and geographic spatial information. The extraction unit is configured to extract influencing factors of the oilfield land value analysis based on the knowledge graph network reflecting the value influencing factors of the oilfield land and actual oilfield land evaluation data of a target region, the influencing factors including land types, reserves conditions, ground development difficulty, land input and land output. The determination unit is configured to determine a comprehensive evaluation coefficient of the influencing factors based on an analytic hierarchy process and expert scoring. The analysis unit is configured to analyze the value of the oilfield land based on the comprehensive evaluation coefficient. 13.An electronic device, comprising at least one processor and at least one memory data-connected with the processor, wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-11.

14. A computer storable medium, characterized by The computer instructions stored on the storable medium are executed by the processor to specifically perform the steps in the method of any one of claims 1-11.

15. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to specifically perform the steps in the method of any one of claims 1-11.

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