中国石油网消息：3月25日，在长庆油田一处井场施工现场，压裂泵轰鸣，施工人员紧张而有序地忙碌着。此时，在300公里外的长庆油田油气工艺研究院的一间办公室内，技术人员正盯着电脑屏幕，通过远程系统操控着现场的每一个施工环节。

China Oil News: On March 25th, at a construction site in a well field of Changqing Oilfield, the hydraulic fracturing pumps were roaring, and the construction workers were working intensively and orderly. At the same time, in an office of the Changqing Oilfield Oil and Gas Technology Research Institute 300 kilometers away, the technicians were staring at the computer screen and controlling every construction process at the site through the remote system.

　　“这套远程系统就像是技术人员的千里眼和顺风耳，能实现对压裂施工全过程的精准感知与超前预警，让地下的复杂工况变得清晰可辨。”长庆油田油气工艺研究院数字化与信息技术研究所负责人刘天宇介绍说，这便是长庆油田自主研发的压裂施工智能预警与全过程决策技术系统。

This remote system is like a telepathic eye and a listening ear for technicians, enabling precise perception and early warning of the entire fracturing construction process, making the complex underground conditions clearly distinguishable. Liu Tianyu, the head of the Digitalization and Information Technology Research Institute of the Oil and Gas Process Technology Research Institute of Changqing Oilfield, introduced that this is the intelligent warning and full-process decision-making technology system for fracturing construction independently developed by Changqing Oilfield.

　　作为典型的低渗、低压、低丰度“三低”油气田，近5年来，长庆油田累计完成压裂作业超2万口井、10万层段。压裂既是释放储层产能的“关键一招”，也是风险高、难度大、成本高的“攻坚环节”。

As a typical "three-low" oil and gas field with low permeability, low pressure and low reserves, in the past five years, the Changqing Oilfield has completed over 20,000 fracturing operations and 100,000 layers of sections. Fracturing is not only the "key move" for releasing reservoir production capacity, but also a "critical stage" with high risks, great difficulties and high costs.

　　面对传统压裂智能化诊断水平低、技术支撑需求激增的现状，长庆油田锚定科技攻关目标，将物联网、大数据、人工智能与压裂工艺深度融合，搭建起“远程监控、智能识别、工况预警、地下模拟”一体化智能决策体系，推动压裂作业从“经验驱动”向“数据驱动”智慧升级。

In response to the current situation where the intelligent diagnosis level of traditional fracturing is low and the demand for technical support is surging, Changqing Oilfield has set the goal of technological research and development. It integrates the Internet of Things, big data, artificial intelligence with fracturing technology to build an integrated intelligent decision-making system featuring "remote monitoring, intelligent identification, condition warning, and underground simulation", promoting the transformation of fracturing operations from "experience-driven" to "data-driven" intelligent upgrading.

　　这套“智慧大脑”的核心涵盖压裂支撑剂现场快速检测、压裂施工曲线远程实时监控、压裂工况智能预警和物理压裂井“数字孪生”四大板块，通过成功攻克多源异构数据解析、秒级曲线绘制、AI智能预警等行业难题，就像为压裂作业打造了“检测员+监控员+预警员+模拟师”的全流程智能团队。

The core of this "intelligent brain" consists of four major sections: on-site rapid detection of fracturing support materials, remote real-time monitoring of fracturing construction curves, intelligent early warning of fracturing conditions, and "digital twin" of physical fracturing wells. By successfully overcoming industry challenges such as multi-source heterogeneous data analysis, second-second-level curve drawing, and AI intelligent early warning, it is like creating a "detector + monitor + early warning officer + simulator" full-process intelligent team for fracturing operations.

　　工况智能预警技术堪称“核心本领”。针对“一人多井”远程监控模式，该技术涵盖大数据标准曲线预警、专家经验算法预警等8种预警方式，报警参数可按需定制，形成“少人多井”的远程技术支撑新模式。通过大数据和神经网络算法拟合“标准曲线模板”，实时比对井下压力曲线，精准识别油（套）管压力异常。

The intelligent condition monitoring technology is regarded as the "core competence". For the "one person controlling multiple wells" remote monitoring mode, this technology encompasses 8 types of warning methods such as big data standard curve warning and expert experience algorithm warning. The alarm parameters can be customized as needed, forming a new remote technical support model of "few personnel managing many wells". By fitting the "standard curve template" with big data and neural network algorithms, the underground pressure curves are compared in real time to accurately identify abnormal oil (casing) pressure.

　　值得一提的是，团队在行业内率先实现压裂过程中破压值的实时识别——打破了“事后才能判断破压”的行业局限。技术人员可在网页端自主设定预警阈值，重点监测压力高低异常、砂浓度骤升骤降等风险点，实现风险的提前预判。

It is worth noting that the team was the first in the industry to achieve real-time identification of the pressure-breaking value during the fracturing process - breaking the industry limitation of "judging the pressure-breaking only after the fact". Technicians can set the warning threshold independently on the web page, focusing on monitoring abnormal pressure levels, sudden increases or decreases in sand concentration, and other risk points, and achieving early prediction of risks.

　　在此基础上，油田创新构建大数据拟合、专家经验、深度学习三重预警模型，可精准预判压力异常、坐封失效等8类高危工况，预警准确率达84.4%。同时，建成的物理井“数字孪生”系统，像是给地下储层打造了数字镜像，让原本看不见、摸不着的地下裂缝，在屏幕上清晰呈现、动态模拟，为施工决策提供直观支撑。

Based on this, the oilfield innovatively constructed a triple warning model integrating big data fitting, expert experience, and deep learning. This model can accurately predict 8 types of high-risk conditions such as abnormal pressure and well setting failure, with a warning accuracy rate of 84.4%. At the same time, the physical well "digital twin" system, which was established, is like creating a digital mirror for the underground reservoir. It enables previously invisible and intangible underground fractures to be clearly presented and dynamically simulated on the screen, providing intuitive support for construction decisions.

　　从“人盯井”到“数管井”，从“事后分析”到“超前预警”，智能压裂技术带来了生产模式的深刻变革。

From "man watching the well" to "numerical management of the well", and from "post-event analysis" to "pre-emptive warning", the intelligent fracturing technology has brought about a profound transformation in the production mode.

　　截至目前，该技术已在长庆油田31个生产单位规模化应用，近3年累计创造增产效益超亿元，推动压裂技术从“现场奔波”转向“远程协同”，从“经验决策”升级为“数据决策”，成为油田降本、提质、增效的核心支撑。

As of now, this technology has been applied on a large scale in 31 production units of the Changqing Oilfield. Over the past three years, it has generated cumulative production increase benefits of over 100 million yuan, shifting the fracturing technology from "on-site traveling" to "remote collaboration", and upgrading from "experience-based decision-making" to "data-based decision-making", becoming the core support for the oilfield's cost reduction, quality improvement, and efficiency enhancement.