A full life cycle operation and maintenance management system for an oil downhole transmitter

Abstract

The present application relates to the technical field of informationization of oil exploration and development and digital processing of whole life cycle management of industrial assets, in particular to a whole life cycle operation and maintenance management system for an oil downhole transmitter, comprising: an asset data management module for building a whole life cycle data lake serving asset value analysis; a benchmark valuation modeling module for generating a theoretical asset value curve of the transmitter under the assumption of no environmental stress; a risk quantification correction module for outputting a risk-adjusted theoretical residual value; a management performance analysis module for calculating a management performance residual after stripping the influence of environmental risks; a decision and execution module for automatically generating differentiated management instructions for asset disposal, inventory strategy adjustment or supplier evaluation, and driving downstream enterprise resource planning systems or inventory control systems to perform closed-loop operations; the present application effectively solves the phenomenon of shirking operation and maintenance responsibilities due to poor well conditions.

Description

Technical Field

[0001] This invention relates to the field of information technology for petroleum exploration and development and digital processing technology for the whole life cycle management of industrial assets, specifically a whole life cycle operation and maintenance management system for oil well downhole transmitters. Background Technology

[0002] With the continuous evolution of oil drilling technology, downhole transmitters, as core monitoring equipment to ensure operational safety and efficiency, are increasingly deployed on a large scale and with increasing technical complexity. In harsh downhole conditions with extremely high temperatures, high pressures, severe vibrations, and strong corrosion, the real-time health status and value evolution of assets exhibit high dynamism and uncertainty.

[0003] Currently, oil companies typically manage downhole equipment using a combination of financial depreciation and regular manual maintenance. This model relies heavily on the enterprise resource planning system to record static attributes, with technicians maintaining downhole equipment based on experience or fixed cycles. However, downhole physical condition data and financial asset management data are often isolated, resulting in significant data silos. Traditional methods struggle to quantify the specific depreciation of asset value caused by extreme physical environmental stresses, making it difficult for management to accurately determine whether equipment failure stems from uncontrollable environmental factors, procurement defects, or poor maintenance. Therefore, accurately quantifying the impact of environmental risks on asset value in complex downhole environments and effectively filtering out environmental noise to accurately reflect management performance has become a pressing technical challenge in the field of oil asset operation and maintenance management. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a full lifecycle operation and maintenance management system for oil well downhole transmitters. Specifically, the technical solution of this invention includes:

[0005] Asset data governance module: used to aggregate static attribute data from asset ledgers, dynamic operating condition data from downhole monitoring networks, and historical event data from operation and maintenance records to build a full lifecycle data lake for asset value analysis;

[0006] Benchmark valuation modeling module: Based on the financial cost and rated life parameters in the static attribute data, it constructs a standard depreciation model and generates the theoretical asset value curve of the transmitter under the assumption of no environmental stress.

[0007] Risk Quantification and Correction Module: This module calls upon the built-in material stress knowledge base to perform quantitative analysis on the dynamic working condition data, calculate the asset value loss caused by environmental risk factors, and correct the theoretical asset value curve based on the loss, outputting the risk-adjusted theoretical residual value.

[0008] Management performance analysis module: used to extract actual performance indicators that characterize the health status of equipment from the historical event data. By comparing the actual performance indicators with the theoretical residual value, the management performance residual after removing the impact of environmental risks is calculated. The residual is used to attribute subjective factors to quality defects or operation and maintenance level.

[0009] Decision and Execution Module: Based on the numerical range of the management performance residual, it automatically generates differentiated management instructions for asset disposal, inventory strategy adjustment, or supplier evaluation, and drives the downstream enterprise resource planning system or inventory control system to perform closed-loop operations.

[0010] Preferably, the system's operating logic is implemented through the following enterprise asset management methods:

[0011] S1. Data integration steps: Collect static attribute data of the target transmitter, dynamic operating condition data of its deployment environment and historical operation and maintenance event data, perform time-series alignment and structured processing to form a panoramic data view for asset analysis.

[0012] S2, Benchmark Valuation Step: Based on the static attribute data, run the preset financial depreciation or performance degradation algorithm to generate a theoretical benchmark value that only changes over time;

[0013] S3. Risk adjustment step: Input the dynamic operating condition data into the environmental impact quantification model, calculate the cumulative risk loss value, and deduct the loss value from the theoretical baseline value to obtain the theoretical residual value after environmental risk adjustment.

[0014] S4. Performance analysis steps: Analyze the historical operation and maintenance event data to obtain the actual status score of the equipment, calculate the deviation between the actual status score and the theoretical residual value, and define it as the management performance residual, which is used to quantify non-environmental management responsibility.

[0015] S5. Intelligent decision-making steps: Input the management performance residual into the preset business rule engine, and automatically trigger the corresponding asset management actions according to its value range, including generating a scrap approval process, triggering a spare parts purchase order, or marking abnormal assets and starting an audit process.

[0016] Preferably, S1 specifically includes:

[0017] Connect with the enterprise's asset master data management system and production data acquisition system;

[0018] Acquire static attribute data, which includes at least the transmitter's asset code, purchase cost, depreciation period, supplier information, and technical specifications.

[0019] Acquire dynamic operating condition data, which includes real-time downhole temperature, pressure, vibration, and media corrosiveness monitoring data;

[0020] Acquire historical operation and maintenance event data, which includes installation date, maintenance records, fault reports, and final scrapping date;

[0021] Clean, correlate, and time-series multi-source data to establish a unified data analysis foundation centered on a single asset identifier.

[0022] Preferably, S3 specifically includes:

[0023] S31. Extract dynamic operating condition data from the panoramic data view and identify key risk factors affecting asset lifespan;

[0024] S32. Query the preset industry material failure model and accelerated aging coefficient, perform weighted integration on the key risk factors, and quantify the cumulative risk loss value within a specific time window.

[0025] S33. Perform value correction calculation: Using the theoretical benchmark value output from step S2 as a basis, subtract the accumulated risk loss value to obtain the theoretical residual value that the asset should hold after bearing actual environmental risks.

[0026] Preferably, S5 specifically includes:

[0027] S51. Preset thresholds for judging management performance, including negative warning thresholds and positive excellence thresholds;

[0028] S52. Determine whether the management performance residual falls within the normal range formed by the negative warning threshold and the positive excellent threshold;

[0029] S53. If so, the asset depreciation is determined to be in line with expectations, and a standard asset scrapping and replacement recommendation process is generated.

[0030] S54. If the management performance residual is lower than the negative warning threshold, it is determined that the asset has abnormal depreciation, a management alarm containing batch quality warning or operation compliance review is generated, the circulation of the same batch of assets is automatically frozen, and a claim or accountability work order is created.

[0031] S55. If the management performance residual is higher than the positive excellent threshold, the asset performance is determined to be better than the risk-adjusted expectation, an asset value revaluation suggestion is generated, and the depreciation policy or maintenance strategy of the asset model can be optimized.

[0032] Preferably, the system also executes supply chain collaborative optimization methods, specifically including:

[0033] Monitor the trend of the cumulative risk loss value calculated in S3;

[0034] Based on the aforementioned trend, predict the risk of spare parts consumption within a specific future time period;

[0035] When trends indicate escalating environmental risks, recommendations are automatically generated to shorten the lead time for relevant spare parts procurement and increase safety stock levels.

[0036] When trends indicate that environmental risks are stable or decreasing, suggestions are automatically generated to reduce inventory levels and optimize capital occupation.

[0037] Preferably, S4 specifically includes:

[0038] Design a dual-path comparison analysis logic;

[0039] The first path calculates the overall deviation of the actual state score from the theoretical baseline value;

[0040] The risk-adjusted deviation between the theoretical residual value calculated by the second path and the theoretical benchmark value;

[0041] The management performance residual is obtained by subtracting the overall deviation from the risk-adjusted deviation.

[0042] The management performance residual is specifically used to assess the value changes caused by controllable factors throughout the asset management process, after excluding objective environmental risks.

[0043] Preferably, S2 specifically includes:

[0044] Set the initial value of the transmitter to its original financial book value or its full performance score;

[0045] Use either straight-line depreciation or a non-linear value decay function based on reliability;

[0046] Using service duration as the sole input variable, the process of value diminishing under an ideal reference environment is simulated.

[0047] Output the theoretical baseline value of the asset under ideal conditions at the current point in time.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This invention constructs a physical-economic mapping engine to transform complex downhole physical conditions such as temperature, pressure, vibration, and corrosion into calculable asset depreciation. By utilizing a built-in material stress knowledge base to perform weighted integration on real-time dynamic data, it corrects the limitations of traditional financial depreciation models in failing to reflect environmental stress. This enables the system to accurately identify the dynamic evolution of asset value under extreme conditions, providing a scientific basis for the preventive replacement of equipment.

[0050] 2. This invention innovatively introduces management performance residual analysis logic, effectively removing the inevitable losses caused by the objective environment through dual-path comparison; this de-environmentalized evaluation model can accurately restore the subjective value deviation caused by product quality defects or low operation and maintenance level, effectively eliminating the phenomenon of shirking operation and maintenance responsibility due to poor well conditions, and providing irrefutable and fair data support for supplier evaluation, internal performance assessment and quality accountability.

[0051] 3. This invention establishes a closed-loop management mechanism from risk perception to intelligent execution. Based on the range determination of management performance residuals, differentiated management instructions can be automatically triggered, such as automatically freezing abnormal batches of assets, generating claim work orders, or initiating audit processes. This intelligent diversion mechanism reduces the lag of manual intervention and, while ensuring the safety of asset operation, significantly improves the economic benefits throughout the entire asset lifecycle by dynamically adjusting depreciation and maintenance strategies.

[0052] 4. This invention achieves deep integration of static financial data, dynamic operating conditions data, and historical maintenance data through a full lifecycle data lake; it establishes a panoramic data view with a single asset identifier as the core, eliminating the problem of disconnect between financial and engineering data in traditional management; at the same time, the system can dynamically link the supply chain according to environmental risk trends, automatically adjust spare parts inventory levels and procurement cycles, and ensure production continuity while reducing capital occupation. Attached Figure Description

[0053] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0054] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0056] Example 1:

[0057] Please see Figure 1 A full lifecycle operation and maintenance management system for oil well downhole transmitters, the system being a digital processing system for enterprise asset operation and maintenance management, comprising:

[0058] Asset data governance module: used to aggregate static attribute data from asset ledgers, dynamic operating condition data from downhole monitoring networks, and historical event data from operation and maintenance records to build a full lifecycle data lake for asset value analysis;

[0059] Benchmark valuation modeling module: Based on the financial cost and rated life parameters in the static attribute data, it constructs a standard depreciation model and generates the theoretical asset value curve of the transmitter under the assumption of no environmental stress.

[0060] Risk Quantification and Correction Module: This module calls upon the built-in material stress knowledge base to perform quantitative analysis on the dynamic working condition data, calculate the asset value loss caused by environmental risk factors, and correct the theoretical asset value curve based on the loss, outputting the risk-adjusted theoretical residual value.

[0061] Management performance analysis module: used to extract actual performance indicators that characterize the health status of equipment from the historical event data. By comparing the actual performance indicators with the theoretical residual value, the management performance residual after removing the impact of environmental risks is calculated. The residual is used to attribute subjective factors to quality defects or operation and maintenance level.

[0062] Decision and Execution Module: Based on the numerical range of the management performance residual, it automatically generates differentiated management instructions for asset disposal, inventory strategy adjustment, or supplier evaluation, and drives the downstream enterprise resource planning system or inventory control system to perform closed-loop operations.

[0063] This embodiment provides a full lifecycle operation and maintenance management system for oil well downhole transmitters. The system is configured as a digital processing system running on an enterprise-level server or cloud computing platform. Its core logical architecture relies on comparing dual value curves to remove environmental noise. The asset data governance module serves as the system's foundational data base, aiming to solve the problem of downhole data silos and establish a unified analytical benchmark. This module uses ETL tools to aggregate three types of heterogeneous data in real time: static attribute data from the asset ledger of the ERP system, dynamic operating condition data from the downhole monitoring network of the SCADA system, and historical event data from the operation and maintenance records of the EAM system. After being cleaned, the above data is stored in a full lifecycle data lake and linked using a unique asset ID as an index.

[0064] The benchmark valuation modeling module is used to establish a value reference system under ideal conditions. Based on the financial cost and rated life parameters in the static attribute data, this module constructs a standard depreciation model. This model does not consider any additional environmental stresses and only assumes that the transmitter operates in a standard laboratory environment, thereby generating a theoretical asset value curve that decreases monotonically over time.

[0065] The risk quantification and correction module, as the core physical-economic mapping engine of this invention, aims to transform uncontrollable physical environments into calculable value deductions. This module calls upon the built-in material stress knowledge base, which includes industry-standard Arrhenius or Coffin-Manson fatigue models, to perform quantitative analysis on dynamic working condition data. Specifically, this module calculates the asset value deduction caused by environmental risk factors and, based on this deduction, adjusts the theoretical asset value curve downwards, outputting the risk-adjusted theoretical residual value.

[0066] Based on this, the management performance analysis module is used to achieve de-environmentalized performance evaluation; it extracts actual performance indicators that characterize the health status of equipment from historical event data; by comparing the actual performance indicators with the theoretical residual value, the management performance residual is calculated; the residual refers to the value deviation remaining after eliminating the inevitable losses caused by the objective environment, which is specifically used to attribute subjective factors such as quality defects or operation and maintenance level.

[0067] The decision-making and execution module is used for closed-loop automated management based on the calculation results; based on the numerical range of the management performance residual, this module automatically generates differentiated management instructions and drives the downstream ERP or inventory control system to execute operations through the API interface;

[0068] This embodiment achieves quantitative decoupling of environmental damage and management errors in oil drilling scenarios by constructing a dual-track data processing mechanism that combines static attributes and dynamic operating conditions. The system utilizes the aggregation capabilities of a full lifecycle data lake to eliminate the information asymmetry caused by the separation of physical operating conditions and financial accounts in traditional asset management. In particular, the introduction of the risk quantification and correction module enables the system to accurately identify prematurely aging equipment that is masked by environmental factors in extremely harsh downhole environments. This avoids misjudging normal rapid wear and tear caused by poor well conditions as equipment quality problems, and also eliminates the phenomenon of maintenance personnel shirking responsibility for early failures caused by improper maintenance by attributing them to objective environmental factors. This significantly improves the scientificity and fairness of asset liability determination.

[0069] Example 2:

[0070] The system's operational logic is implemented through the following enterprise asset management methods:

[0071] S1. Data integration steps: Collect static attribute data of the target transmitter, dynamic operating condition data of its deployment environment and historical operation and maintenance event data, perform time-series alignment and structured processing to form a panoramic data view for asset analysis.

[0072] S2, Benchmark Valuation Step: Based on the static attribute data, run the preset financial depreciation or performance degradation algorithm to generate a theoretical benchmark value that only changes over time;

[0073] S3. Risk adjustment step: Input the dynamic operating condition data into the environmental impact quantification model, calculate the cumulative risk loss value, and deduct the loss value from the theoretical baseline value to obtain the theoretical residual value after environmental risk adjustment.

[0074] S4. Performance analysis steps: Analyze the historical operation and maintenance event data to obtain the actual status score of the equipment, calculate the deviation between the actual status score and the theoretical residual value, and define it as the management performance residual, which is used to quantify non-environmental management responsibility.

[0075] S5. Intelligent decision-making steps: Input the management performance residual into the preset business rule engine, and automatically trigger the corresponding asset management actions according to its value range, including generating a scrap approval process, triggering a spare parts purchase order, or marking abnormal assets and starting an audit process.

[0076] This embodiment details the operating logic of the above system, namely the enterprise asset management method, which includes the following strict time sequence steps: Execute the S1 data integration step, in which the system collects static attribute data, dynamic operating condition data, and historical operation and maintenance event data of the target transmitter; the key to this step is to perform time sequence alignment, that is, to ensure that the timestamp of the operating condition data is accurately matched with the time window of the operation and maintenance event to form a panoramic data view.

[0077] The S2 benchmark valuation step is executed, which uses a preset algorithm based on static attributes to generate a theoretical benchmark value that changes only over time; this step establishes the value baseline under perfect conditions. The S3 risk adjustment step is executed, which inputs dynamic operating condition data into the environmental impact quantification model; the system calculates the cumulative risk loss value and deducts this value from the theoretical benchmark value to obtain the theoretical residual value after environmental risk adjustment; this step completes the mapping from ideal to real physical constraints.

[0078] Based on this, the S4 performance analysis step is executed to analyze the operation and maintenance data to obtain the actual status score; the deviation between this score and the theoretical residual value is calculated and defined as the management performance residual; this step transforms the deviation in the physical world into a responsibility indicator in the management world; the S5 intelligent decision-making step is executed to input the management performance residual into the business rules engine; in response to the residual being within the normal range, scrap approval is triggered; in response to the residual being abnormally low, spare parts procurement or auditing processes are triggered.

[0079] This embodiment establishes a standardized data processing pipeline, transforming the complex downhole asset management process into calculable, predictable, and traceable digital logic. Through strict time-series alignment and structured processing, this method solves the problem of misalignment in the time dimension of multi-source heterogeneous data, ensuring the contextual consistency of subsequent value analysis. In particular, the layer-by-layer stripping logic from theoretical benchmark value to risk-adjusted residual value realizes the transformation from experience-driven to data-driven, enabling managers to clearly see the real reasons for the loss of value of each asset and greatly reducing the uncertainty of decision-making.

[0080] Example 3:

[0081] S1 specifically includes:

[0082] Connect to the enterprise's asset master data management system and production data acquisition system; acquire static attribute data, which includes at least the transmitter's asset code, purchase cost, depreciation period, supplier information, and technical specifications;

[0083] Acquire dynamic operating condition data, which includes real-time downhole temperature, pressure, vibration, and media corrosiveness monitoring data;

[0084] Acquire historical operation and maintenance event data, which includes installation date, maintenance records, fault reports, and final scrapping date;

[0085] Clean, correlate, and time-series multi-source data to establish a unified data analysis foundation centered on a single asset identifier.

[0086] This embodiment specifies the S1 data integration steps; the system connects to the enterprise's Asset Master Data Management System (MDM) and Production Data Acquisition System (SCADA) via the Industrial Bus Protocol; the system performs multi-dimensional data content definition and acquisition.

[0087] For static attribute data, the system must acquire a dataset that includes asset code, purchase cost, depreciation period, supplier information, and technical specifications. Among these, the asset code is used for unique identification, the purchase cost is used for financial calculations, and the technical specifications, such as maximum temperature resistance, rated pressure, seismic resistance level, and corrosion resistance threshold, are used for subsequent physical limit comparison and acceleration factor calculation.

[0088] For dynamic operating condition data, the system acquires downhole sensor array data including real-time temperature, pressure, vibration and media corrosion monitoring data. Among them, real-time temperature data is used to assess the life of electronic components, pressure data is used to calculate the stress load of pressure-bearing components, vibration data is used to assess mechanical fatigue, and media corrosion monitoring data such as H2S and CO2 partial pressure are used to assess shell life.

[0089] For historical operation and maintenance event data, the system obtains records including installation date, maintenance records, fault reports, and final scrapping date;

[0090] The system preprocesses multi-source data, including cleaning operations to remove sensor noise, association operations based on asset code Join tables, and time serialization operations, thereby establishing a unified data analysis foundation with a single asset identifier as the core.

[0091] This embodiment ensures that subsequent model calculations have sufficient and critical input variables by clearly defining the specific dimensions of the three types of data. In particular, the introduction of media corrosion and vibration data significantly improves the completeness of the characterization of the complex physical environment downhole and fills the blind spot of traditional management that only focuses on temperature and pressure. This unified data foundation with a single asset identifier at its core effectively breaks down the barriers between financial data and engineering data, and provides solid data support for accurate traceability throughout the entire life cycle.

[0092] Example 4:

[0093] S3 specifically includes:

[0094] S31. Extract dynamic operating condition data from the panoramic data view and identify key risk factors affecting asset lifespan;

[0095] S32. Query the preset industry material failure model and accelerated aging coefficient, perform weighted integration on the key risk factors, and quantify the cumulative risk loss value within a specific time window.

[0096] S33. Perform value correction calculation: Take the theoretical benchmark value output from step S2 as the basis, subtract the accumulated risk loss value, and thus obtain the theoretical residual value that the asset should hold after bearing actual environmental risks.

[0097] This embodiment specifies the S3 risk adjustment step, which is a key step connecting the physical world and digital value; by executing S31, the system extracts dynamic operating condition data from the panoramic data view and identifies key risk factors affecting asset lifespan. ;

[0098] Executing S32, this step aims to quantify the hidden environmental damage to equipment; the system introduces a cumulative risk loss value. The calculation logic is as follows:

[0099]

[0100] To enable algorithm reproduction in digital processing systems, the integral formula is discretized using the rectangular numerical integration method. ,in, The sampling period for sensor data. This represents the total number of samples.

[0101] in, The source is a pre-set industry material failure model, and the physical meaning is the first... The weighting coefficients of risk factors are typically normalized. These coefficients are determined using the Analytic Hierarchy Process (AHP) combined with prior knowledge of downhole operating conditions. For example, in high-temperature, high-sulfur well scenarios, the temperature weight is determined by constructing a judgment matrix. Corrosion weight Vibration weight and pressure weight The proportional relationship, typically taking the value , , , And satisfy ;

[0102] The source is dynamic operating condition data, and the physical meaning is... The first moment Similar operating condition data;

[0103] The source is a preset nonlinear function, whose physical meaning is an accelerated aging coefficient, used to describe how environmental stress accelerates equipment wear; to ensure the computability of the model and complete coverage of the pressure and media corrosivity data described in Example 3, this system has built-in explicit function mapping relationships and typical parameter value ranges:

[0104] For temperature factor Using the Arrhenius equation form: ,in, This is the rated reference absolute temperature, in Kelvin (K). For real-time monitoring of absolute temperature, the unit is Kelvin (K). The activation energy for chip materials, for conventional downhole electronic components, typically ranges from 0.3eV to 1.1eV, with a typical value of 0.7eV; Here is the Boltzmann constant, with a value of approximately ;

[0105] For vibration factors An inverse power-law model is adopted: ,in The rated seismic resistance level, This is a material fatigue index, specifically for stainless steel or titanium alloy shell structures. The typical value range is 2.5 to 4.0;

[0106] For media corrosive agents The partial pressure of H2S / CO2 in Example 3, or the partial pressure value calculated from the content combined with the real-time total pressure, is determined using a linear permeation model: ;in, For safe concentration thresholds, Based on the NACEMR0175 / ISO15156 industry standard, this specification applies to transmitters made of conventional stainless steel. Safety voltage threshold Set as ,about This serves as the starting point for accelerating corrosion risk calculations. This is the corrosion rate constant, typically ranging from 0.01 to 0.5. This is the osmosis order, which is used here to avoid confusion with the pressure symbol. To avoid confusion, Greek letters were specifically chosen. Replace the original general symbols It is usually set to 1.0 or 0.5;

[0107] For stress factors A standardized stress model is adopted: Only when Effective at time This is the pressure sensitivity coefficient, which typically ranges from 1.5 to 3.0 depending on the material of the seal. Rated pressure;

[0108] The source is financial parameters, and the physical meaning is the standard loss cost per unit time; this is explicitly stated here. The source of the acquisition is That is, the initial value divided by the rated lifespan to ensure that the dimensions of the integral result are consistent with the asset value; it should be noted here that the formula uses This is to isolate the additional loss rate caused solely by environmental stress, assuming the environment is at its rated state. If the integral term is 0, it avoids double counting with the natural depreciation of the time dimension already included in step S2.

[0109] The physical meaning is the total number of key risk factors identified by the system;

[0110] The physical meaning is the integration time variable, used to iterate from time 0 to the present. A continuous time interval of a moment;

[0111] Perform the S33 value correction calculation, using the following formula:

[0112]

[0113] The system uses the theoretical benchmark value output from step S2 as a basis, subtracts the accumulated risk loss value, and thus obtains the theoretical residual value that the asset should hold after bearing actual environmental risks. ;

[0114] This embodiment introduces integral calculations and a clearly defined physical acceleration aging coefficient, covering four-dimensional variables: temperature, vibration, corrosion, and pressure, to accurately capture the cumulative effect of environmental stress over time. This method not only considers the impact of instantaneous extreme working conditions but also quantifies the internal damage caused by the superposition of long-term minor stresses. In particular, the supplementary modeling for corrosion and pressure, as well as the clear definition of key physical constants such as activation energy and fatigue index, achieve full coverage of complex downhole working conditions. Furthermore, by strictly defining temperature units and mathematical symbols, the uncertainty of the calculation model is eliminated, providing a scientific quantitative basis for the preventive replacement of assets.

[0115] Example 5:

[0116] S5 specifically includes:

[0117] S51. Preset thresholds for judging management performance, including negative warning thresholds and positive excellence thresholds;

[0118] S52. Determine whether the management performance residual falls within the normal range formed by the negative warning threshold and the positive excellent threshold;

[0119] S53. If so, the asset depreciation is determined to be in line with expectations, and a standard asset scrapping and replacement recommendation process is generated.

[0120] S54. If the management performance residual is lower than the negative warning threshold, it is determined that the asset has abnormal depreciation, a management alarm containing batch quality warning or operation compliance review is generated, the circulation of the same batch of assets is automatically frozen, and a claim or accountability work order is created.

[0121] S55. If the management performance residual is higher than the positive excellent threshold, the asset performance is determined to be better than the risk-adjusted expectation, an asset value revaluation suggestion is generated, and the depreciation policy or maintenance strategy of the asset model can be optimized.

[0122] This embodiment specifies the S5 intelligent decision-making steps; when executing S51, the system presets management performance judgment thresholds, including negative warning thresholds. With positive superior threshold To ensure the objectivity and feasibility of the threshold setting, this system uses either a relative proportion method based on the initial asset value or a statistical distribution method based on historical data to determine the aforementioned threshold.

[0123] Relative proportion method: setting , ,in The initial value of the asset. The preset coefficient has a typical value of [value]. This means that an alarm is triggered when the value deviation caused by management factors exceeds 5%-10% of the original asset value;

[0124] Statistical distribution method: setting , ,in This is the standard deviation of the historical management performance residuals of this transmitter model, used to dynamically identify abnormal individuals that deviate from the normal distribution;

[0125] By executing S52 and S53, the system determines the residual performance of management. Whether it falls within the normal range, i.e. In response to falling into this range, the system determines that the asset wear and tear is in line with expectations, that is, the equipment performance matches the poor well conditions, and then generates a standard asset scrapping and replacement recommendation process without triggering additional audits.

[0126] Execute S54 in response to the management performance residual falling below the negative warning threshold, i.e. The system determines that the asset has abnormal depreciation, which means that even considering harsh environments, the failure rate of the equipment still exceeds the theoretical range. The system generates management alerts that include batch quality warnings or operational compliance reviews, and automatically executes freeze transfer operations, locks the same batch of assets in the ERP, and creates a claim task for the supplier or an accountability work order for the operation and maintenance team.

[0127] Implementing S55 in response to management performance residuals exceeding a positive excellence threshold, i.e. The system determines that the asset's performance is better than the risk-adjusted expectations; the system generates an asset value revaluation suggestion and prompts the manager to optimize the depreciation policy or maintenance strategy for this type of asset.

[0128] This embodiment, based on a residual interval determination mechanism, achieves automatic asset allocation and intelligent decision-making in asset management. This mechanism not only automatically identifies bad assets for timely loss mitigation and risk prevention, but also identifies good assets for strategy optimization. In particular, through explicit threshold calculation logic, such as the percentage method or standard deviation method, the judgment criteria are no longer vague subjective experience, but rather quantifiable mathematical boundaries. By automatically freezing the flow of assets and generating accountability work orders, the system constructs a robust quality defense line to prevent substandard products from being reused. Simultaneously, the revaluation recommendations for superior assets help enterprises dynamically adjust their depreciation models, maximizing the economic benefits throughout the asset's lifecycle.

[0129] Example 6:

[0130] The system also implements supply chain collaborative optimization methods, specifically including:

[0131] Monitor the trend of the cumulative risk loss value calculated in S3; predict the spare parts consumption risk in a specific future time period based on the trend; when the trend shows that the environmental risk is intensifying, automatically generate suggestions to shorten the procurement lead time of relevant spare parts and increase the safety stock level; when the trend shows that the environmental risk is stable or decreasing, automatically generate suggestions to reduce the inventory level and optimize capital occupation.

[0132] This embodiment further implements the supply chain collaborative optimization method; the system continuously monitors the cumulative risk loss value calculated in S3. The rate of change trend; the trend is calculated... In the time window First derivative within Definition, that is ,in The value is a sliding window that takes values ​​from the past 24 to 72 hours; when Three consecutive windows exceeding the preset fluctuation threshold When this trend is identified as an escalation of environmental risk, the system predicts the risk of spare parts consumption within a specific future time period based on the trend.

[0133] The system predicts the risk of spare parts consumption within a specific future time period based on the aforementioned trend; in response to the trend indicating an increase in environmental risk, such as a general rise in wellbore temperature leading to... As the growth rate accelerates, the system determines that the frequency of future spare parts demand will increase. At this time, the system automatically generates suggestions to shorten the procurement lead time of relevant spare parts and increase the level of safety stock, aiming to cope with the risk of stockouts caused by the shortened lifespan due to harsh environments.

[0134] In response to trend indicators showing stable or decreasing environmental risks, the system automatically generates suggestions to reduce inventory levels and optimize capital occupation.

[0135] This embodiment transmits the physical risks underground to the surface supply chain system in real time, breaking the traditional fixed-quota reserve model. By establishing a dynamic linkage mechanism between environmental risk perception and inventory management, the system can prepare goods in advance when the environment deteriorates to ensure production continuity, and release redundant inventory when the environment is stable to reduce capital occupation costs, thereby achieving a dual optimization of supply chain resilience and economy.

[0136] Example 7:

[0137] S4 specifically includes:

[0138] Design a dual-path comparison analysis logic;

[0139] The first path calculates the overall deviation of the actual state score from the theoretical baseline value;

[0140] The risk-adjusted deviation between the theoretical residual value calculated by the second path and the theoretical benchmark value;

[0141] The management performance residual is obtained by subtracting the overall deviation from the risk-adjusted deviation.

[0142] The management performance residual is specifically used to assess the value changes caused by controllable factors throughout the asset management process, after excluding objective environmental risks.

[0143] This embodiment provides an in-depth analysis of the logic in the S4 performance analysis steps, particularly the dual-path comparison analysis logic; the system is designed with a dual-path calculation architecture; it is worth noting that, to ensure dimensional consistency in the dual-path calculation, i.e., to avoid directly calculating dimensionless scores with dimensional monetary values, the actual state score in this embodiment... Before being entered into the formula calculation, it has already been mapped to the actual asset value. The specific mapping logic is as follows:

[0144]

[0145] Note: This uses... That is, the theoretical baseline value at the current moment calculated in step S2, rather than... This serves as a baseline to ensure that the actual value score incorporates the factor of natural depreciation over time, preventing logical errors caused by differences in the baseline; among which The normalized health coefficient is derived from the parsing of operation and maintenance records, with a value ranging from 0.0 to 1.0. To ensure the feasibility and objectivity of this coefficient, this system uses an event-weighted deduction method for calculation.

[0146] A default fault level weight table is set up, such as a weight of 0.05 for general faults and 0.2 for severe faults. Fault reports in historical maintenance event data are iterated through, and the weight values ​​of all recorded events are summed to obtain the total deduction. The total deduction is then subtracted from 1 to obtain the final score. The minimum value is not less than 0; to reflect the role of operation and maintenance in restoring asset value, a time decay factor is introduced into the deduction item. ,in For the first Time of occurrence of the fault The coefficient of restitution ranges from 0.01 to 0.05. The current moment is for evaluating the value of the system, so that the negative impact of long-term failures on the current health gradually weakens over time;

[0147] In the first path, the actual state score is calculated, which is the mapped actual value. Compared to theoretical benchmark value Overall deviation The formula is This value represents the total cost of equipment loss compared to its current perfect state.

[0148] In the second path, the theoretical residual value is calculated. Compared to theoretical benchmark value Risk adjustment bias The formula is This value represents the reasonable amount of loss caused purely by adverse environmental conditions;

[0149] The system performs differential calculations, extracting the management performance residuals by subtracting the overall deviation from the risk-adjusted deviation. :

[0150]

[0151] Management performance residuals are specifically used to assess the value changes caused by controllable factors throughout the asset management process, such as procurement quality or maintenance level, after excluding objective environmental risks.

[0152] This embodiment uses the dual-path difference method to completely eliminate environmental interference in mathematical logic under a unified monetary unit [2]; the method, like a scalpel, accurately divides the total depreciation of equipment into natural disasters, i.e., environmental factors, and human-caused disasters, i.e., management or quality factors; through the corrected The mapping logic prevents the system from misjudging the natural depreciation of old equipment as a management error, ensuring the fairness of the attribution analysis and providing irrefutable data for subsequent supplier evaluation and internal performance appraisal.

[0153] Example 8:

[0154] S2 specifically includes:

[0155] Set the initial value of the transmitter to its original financial book value or its full performance score;

[0156] Use either straight-line depreciation or a non-linear value decay function based on reliability;

[0157] Using service duration as the sole input variable, the process of value diminishing under an ideal reference environment is simulated.

[0158] Output the theoretical baseline value of the asset under ideal conditions at the current point in time.

[0159] This embodiment specifies the S2 benchmark valuation step; the system sets the initial value of the transmitter. Its original financial book value or full performance score;

[0160] The system employs either a straight-line depreciation method or a reliability-based nonlinear value decay function for modeling; in this embodiment, a theoretical benchmark value under ideal conditions is defined. as follows:

[0161]

[0162] The source is the difference between the system clock and the installation date, and its physical meaning is the current service duration;

[0163] The source is static attribute data, and the physical meaning is rated life. The symbol L is used here to distinguish it from the symbol T representing temperature in Example 4.

[0164] The source is a preset parameter, and its physical meaning is the attenuation shape factor;

[0165] The system uses service duration as the only input variable to simulate the value decline process under an ideal reference environment and outputs the theoretical benchmark value of the asset at the current point in time under ideal conditions.

[0166] This embodiment provides a flexible and accurate benchmark modeling method that is compatible with the traditional financial straight-line depreciation method and can also simulate the nonlinear performance degradation law of physical equipment. By introducing the decay shape factor, the model can more accurately reflect the value loss characteristics of different types of transmitters at different stages of their life cycle, providing an accurate minuend for subsequent risk deduction and ensuring the objectivity and rationality of the value assessment benchmark.

[0167] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A full lifecycle operation and maintenance management system for oil well downhole transmitters, characterized in that, The system is a digital processing system for enterprise asset operation and maintenance management, including: Asset data governance module: used to aggregate static attribute data from asset ledgers, dynamic operating condition data from downhole monitoring networks, and historical event data from operation and maintenance records to build a full lifecycle data lake for asset value analysis; Benchmark valuation modeling module: Based on the financial cost and rated life parameters in the static attribute data, it constructs a standard depreciation model and generates the theoretical asset value curve of the transmitter under the assumption of no environmental stress. Risk Quantification and Correction Module: This module calls upon the built-in material stress knowledge base to perform quantitative analysis on the dynamic working condition data, calculate the asset value loss caused by environmental risk factors, and correct the theoretical asset value curve based on the loss, outputting the risk-adjusted theoretical residual value. Management performance analysis module: used to extract actual performance indicators that characterize the health status of equipment from the historical event data. By comparing the actual performance indicators with the theoretical residual value, the management performance residual after removing the impact of environmental risks is calculated. The residual is used to attribute subjective factors to quality defects or operation and maintenance level. Decision and Execution Module: Based on the numerical range of the management performance residual, it automatically generates differentiated management instructions for asset disposal, inventory strategy adjustment, or supplier evaluation, and drives the downstream enterprise resource planning system or inventory control system to perform closed-loop operations. Connect with the enterprise's asset master data management system and production data acquisition system; Acquire static attribute data, which includes at least the transmitter's asset code, purchase cost, depreciation period, supplier information, and technical specifications. Acquire dynamic operating condition data, which includes real-time downhole temperature, pressure, vibration, and media corrosiveness monitoring data; Acquire historical operation and maintenance event data, which includes installation date, maintenance records, fault reports, and final scrapping date; Clean, correlate, and time-series the multi-source data to establish a unified data analysis foundation centered on a single asset identifier; Extract dynamic operating condition data from a panoramic data view to identify key risk factors affecting asset lifespan; Query the pre-set industry material failure model and accelerated aging coefficient, perform weighted integration on the key risk factors, and quantify the cumulative risk loss value within a specific time window. Perform value correction calculation: Based on the output theoretical benchmark value, subtract the accumulated risk loss value to obtain the theoretical residual value that the asset should hold after bearing actual environmental risks.

2. The full lifecycle operation and maintenance management system for oil well downhole transmitters according to claim 1, characterized in that, The system's operational logic is implemented through the following enterprise asset management methods: S1. Data integration steps: Collect static attribute data of the target transmitter, dynamic operating condition data of its deployment environment and historical operation and maintenance event data, perform time-series alignment and structured processing to form a panoramic data view for asset analysis. S2, Benchmark Valuation Step: Based on the static attribute data, run the preset financial depreciation or performance degradation algorithm to generate a theoretical benchmark value that only changes over time; S3. Risk adjustment step: Input the dynamic operating condition data into the environmental impact quantification model, calculate the cumulative risk loss value, and deduct the loss value from the theoretical baseline value to obtain the theoretical residual value after environmental risk adjustment. S4. Performance analysis steps: Analyze the historical operation and maintenance event data to obtain the actual status score of the equipment, calculate the deviation between the actual status score and the theoretical residual value, and define it as the management performance residual, which is used to quantify non-environmental management responsibility. S5. Intelligent decision-making steps: Input the management performance residual into the preset business rule engine, and automatically trigger the corresponding asset management actions according to its value range, including generating a scrap approval process, triggering a spare parts purchase order, or marking abnormal assets and starting an audit process.

3. The full lifecycle operation and maintenance management system for oil well downhole transmitters according to claim 2, characterized in that, S5 specifically includes: S51. Preset thresholds for judging management performance, including negative warning thresholds and positive excellence thresholds; S52. Determine whether the management performance residual falls within the normal range formed by the negative warning threshold and the positive excellent threshold; S53. If so, the asset depreciation is determined to be in line with expectations, and a standard asset scrapping and replacement recommendation process is generated. S54. If the management performance residual is lower than the negative warning threshold, it is determined that the asset has abnormal depreciation, a management alarm containing batch quality warning or operation compliance review is generated, the circulation of the same batch of assets is automatically frozen, and a claim or accountability work order is created. S55. If the management performance residual is higher than the positive excellent threshold, the asset performance is determined to be better than the risk-adjusted expectation, an asset value revaluation suggestion is generated, and the depreciation policy or maintenance strategy of the asset that can be optimized is suggested.

4. The full lifecycle operation and maintenance management system for oil well downhole transmitters according to claim 3, characterized in that, The system also executes a supply chain collaborative optimization method, specifically including: Monitor the trend of the cumulative risk loss value calculated in S3; Based on the aforementioned trend, predict the risk of spare parts consumption within a specific future time period; When trends indicate escalating environmental risks, recommendations are automatically generated to shorten the lead time for relevant spare parts procurement and increase safety stock levels. When trends indicate that environmental risks are stable or decreasing, suggestions are automatically generated to reduce inventory levels and optimize capital occupation.

5. The full lifecycle operation and maintenance management system for oil well downhole transmitters according to claim 2, characterized in that, S4 specifically includes: Design a dual-path comparison analysis logic; The first path calculates the overall deviation of the actual state score from the theoretical baseline value; The risk-adjusted deviation between the theoretical residual value calculated by the second path and the theoretical benchmark value; The management performance residual is obtained by subtracting the overall deviation from the risk-adjusted deviation. The management performance residual is specifically used to assess the value changes caused by controllable factors throughout the asset management process, after excluding objective environmental risks.

6. The full lifecycle operation and maintenance management system for oil well downhole transmitters according to claim 2, characterized in that, S2 specifically includes: Set the initial value of the transmitter to its original financial book value or its full performance score; Use either straight-line depreciation or a non-linear value decay function based on reliability; Using service duration as the sole input variable, the process of value diminishing under an ideal reference environment is simulated. Output the theoretical baseline value of the asset under ideal conditions at the current point in time.

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