Architecture of Intelligent Early Warning System for Complex Drilling Risks Based on Digital Twin Technology
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摘要:
为降低深部地层不确定地质条件诱发的钻井井下复杂风险，采用数字孪生技术，构建了基于物理模型与数据驱动模型融合的钻井复杂风险数字孪生智能预警体系。为满足随钻预警、降低风险等实际需求，提出了基于微服务的数据集成、孪生体智能感知、多模态融合预警、孪生体智能诊断等4项数字孪生预警系统支撑技术，建立了钻井数字孪生预警系统的整体架构，并详述了其功能及模型设计，涉及物理设备层、虚拟实体层、孪生体数据层、孪生体算法模型层及孪生体预警服务层5层交互系统，设计了钻前预演规避风险、钻中实时预警、钻后分析区块风险等3种区块钻井优化设计的应用场景，实现了对多源异构数据的数字化集成，传统物理模型与智能模型的多重融合，以及溢流、井漏、卡钻的预警与类型识别等功能，从而达到降低深部钻井作业风险、优快钻井的目的。研究结果表明，基于“模型+数据”的数字孪生预警架构具有提前识别钻井过程中的风险和快速诊断风险类型的潜力，为智能钻井风险预警提供了新的技术途径。
Abstract:To reduce the complex drilling risks induced by uncertain geological conditions in deep formations, the digital twin technology has been adopted to construct a digital twin intelligent risk early warning system for complex drilling risks based on the fusion of physical models and data-driven models. In order to meet the actual requirements of early warning while drilling and risk reduction, four supporting technologies of digital twin early warning system were proposed, including microservice-based data integration, intelligent perception of digital twins, multimodal fusion early warning, and intelligent diagnosis of digital twins. The overall architecture of the digital twin early warning system for drilling has been established, and its functions and model design were described in detail. The system involved a five-layer interaction system, including physical device layer, virtual entity layer, digital twin data layer, digital twin algorithm model layer, and digital twin early warning service layer. Three application scenarios have been designed, including pre-drilling risk avoidance rehearsal, real-time warning during drilling, and post-drilling analysis of block risk situations to optimize block drilling design. This system has achieved several functions, such as digital integration of multi-source heterogeneous data, multiple fusion of traditional physical models with intelligent models, warning and type identification of overflow, lost circulation, and pipe sticking, etc. Therefore, the risks associated with deep drilling operations have been reduced and drilling efficiency has been optimized. The study results indicate that the digital twin warning architecture based on “model + data” has the potential to identify drilling risks and diagnose risk types in advance during drilling, providing new technology approaches for intelligent drilling risk early warning.
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Keywords:
- drilling /
- risks /
- intelligent early warning /
- architecture /
- digital twin
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图 1 微服务“静态+实时”数据集成技术总体架构
Figure 1. Overall architecture of microservices “static + real-time” data integration technology
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图 2 孪生体“更新+优化”智能感知技术流程
Figure 2. Workflow of digital twins “update + optimization” intelligent perception technology
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图 3 钻井复杂风险数字孪生智能预警系统的架构
Figure 3. Architecture of the digital twin intelligent early warning system for complex drilling risks
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图 4 钻井复杂风险数字孪生智能预警系统的运行过程
Figure 4. Operation process of the digital twin intelligent early warning system for complex drilling risks
表 1 复杂风险预警参数表征规律
Table 1 Characterization laws of early warning parameters for complex risks
风险类型 实测参数表征规律 实时计算参数 实时预测参数 溢流 钻时变短悬重降低
总池液面逐渐升高
出口流量逐渐升高
立压逐渐降低然后升高
气测全烃逐渐升高实测立压小于计算立压
井底压力小于孔隙压力实测立压小于预测立压
高压地层识别井漏 总池液面逐渐降低
出口流量逐渐降低
立压逐渐降低实测立压小于计算立压
井底压力大于漏失压力实测立压小于预测立压
井漏曲线案例推理识别井塌卡钻 立压突然升高
扭矩逐渐或突然升高
钻时突然增大实测立压大于计算立压
实测扭矩大于计算扭矩实测立压大于预测立压
易塌地层识别压差卡钻 立压不变
扭矩逐渐升高
起钻悬重升高
下钻悬重降低实测起钻悬重大于计算起钻悬重
实测下钻悬重小于计算下钻悬重实测起钻悬重大于预测起钻悬重
实测下钻悬重小于预测下钻悬重
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