Automatic well test interpretation based on convolutional neural network for a radial composite reservoir

An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network(CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data transformed by logarithm function and the loss function of mean square error(MSE), the optimal CNN is obtained by reducing the loss function to optimize the network with "dropout" method to avoid over fitting. The trained optimal network can be directly used to interpret the buildup or drawdown pressure data of the well in the radial composite reservoir, that is, the log-log plot of the given measured pressure variation and its derivative data are input into the network, the outputs are corresponding reservoir parameters(mobility ratio, storativity ratio, dimensionless composite radius, and dimensionless group characterizing well storage and skin effects), which realizes the automatic initial fitting of well test interpretation parameters. The method is verified with field measured data of Daqing Oilfield. The research shows that the method has high interpretation accuracy, and it is superior to the analytical method and the least square method.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

Introducing Intelligent Agents Potential into a competent Integral Multi-Agent Sensor Network Simulation Architecture Design

During this research we spot several key issues concerning WSN design process and how to introduce intelligence in the motes. Due to the nature of these networks, debugging after deployment is unrealistic, thus an efficient testing method is required. WSN simulators perform the task, but still code implementing mote sensing and RF behaviour consists of layered and/or interacting protocols that for the sake of designing accuracy are tested working as a whole, running on specific hardware. Simulators that provide cross layer simulation and hardware emulation options may be regarded as the last milestone of the WSN design process. Especially mechanisms for introducing intelligence into the WSN decision making process but in the simulation level is an important aspect not tackled so far in the literature at all. The herein proposed multi-agent simulation architecture aims at designing a novel WSN simulation system independent of specific hardware platforms but taking into account all hardware entities and events for testing and analysing the behaviour of a realistic WSN system. Moreover, the design herein outlined involves the basic mechanisms, with regards to memory and data management, towards Prolog interpreter implementation in the simulation level.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

Application and development trend of artificial intelligence in petroleum exploration and development

Aiming at the actual demands of petroleum exploration and development,this paper describes the research progress and application of artificial intelligence(AI)in petroleum exploration and development,and discusses the applications and development directions of AI in the future.Machine learning has been preliminarily applied in lithology identification,logging curve reconstruction,reservoir parameter estimation,and other logging processing and interpretation,exhibiting great potential.Computer vision is effective in picking of seismic first breaks,fault identification,and other seismic processing and interpretation.Deep learning and optimization technology have been applied to reservoir engineering,and realized the real-time optimization of waterflooding development and prediction of oil and gas production.The application of data mining in drilling,completion,and surface facility engineering etc.has resulted in intelligent equipment and integrated software.The potential development directions of artificial intelligence in petroleum exploration and development are intelligent production equipment,automatic processing and interpretation,and professional software platform.The highlights of development will be digital basins,fast intelligent imaging logging tools,intelligent seismic nodal acquisition systems,intelligent rotary-steering drilling,intelligent fracturing technology and equipment,real-time monitoring and control of zonal injection and production.

Interpretable and Adaptable Early Warning Learning Analytics Model

Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain.Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions.Recently,some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified.However,adaptability is not specifically considered in this domain.This paper presents a new framework based on hybrid statistical fuzzy theory to overcome these limitations.It also provides explainability in the form of rules describing the reasoning behind a particular output.The paper also discusses the system evaluation on a benchmark dataset showing promising results.The measure of explainability,fuzzy index,shows that the model is highly interpretable.This system achieves more than 82%recall in both the classification and the context adaptation stages.

Challenges involved in the application of artificial intelligence in gastroenterology:The race is on!

Gastroenterology is a particularly data-rich field,generating vast repositories of data that are a fruitful ground for artificial intelligence(AI)and machine learning(ML)applications.In this opinion review,we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology.Currently,AI/ML-based models have been developed in the following applications:Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease,models employing non-invasive parameters that provide accurate diagnoses aiming to either replace,minimize,or refine the indications of endoscopy,models utilizing genomic data to diagnose various gastrointestinal diseases,computer-aided diagnosis systems facilitating the interpretation of endoscopy images,models to facilitate treatment allocation and predict the response to treatment,and finally,models in prognosis predicting complications,recurrence following treatment,and overall survival.Then,we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology.Specifically,we elaborate on concerns regarding accuracy,cost-effectiveness,cybersecurity,interpretability,oversight,and liability.While AI is unlikely to replace physicians,it will transform the skillset demanded by future physicians to practice.Thus,physicians are expected to engage with AI to avoid becoming obsolete.

深地工程多维信息感知与智能建造的发展与展望

随着大数据、云计算、人工智能等数字技术的加速演进,各领域智能化、信息化、数字化已成为未来的大势所趋。深地工程作为国家重大战略科技问题,必然面临智能化升级。然而,深部岩体“三高一扰动”的复杂特征给深地工程智能化转型带来严峻的挑战。为实现深地工程与数字技术的高效融合,研究基于“感知-传送-解译-分析-决策”的智能化实践路径,系统回顾了地下工程中智能感知、实时传输、信息解译、数据分析、智能决策等领域的代表性研究进展,并针对性提出了“多感知、快响应、大数据、优方法、精模型、强平台、易推广”的深地工程智能建造发展方向。研究表明:①前沿的深地工程感知技术包括:光纤传感器、MEMS传感器、计算机视觉、自动化机器人等,待数据采集完毕后,通过兼具配置简单、容错能力强、可移动性好等优点的无线通信协议完成数据的实时响应,以实现深地工程监测数据的精准感知与实时传输;②深地工程原位监测技术获取的数据类型主要包括图像、波、点云等,对原始数据解译及分析的模型众多,采用新一代的人工智能技术,如:人工神经网络和深度学习技术,可显著提高解译与分析的效率;③智能决策系统具备高效的学习能力,能够适应复杂环境下的不确定性,通过循环自主学习,以进行决策问题的智能解答。当前,我国深地工程智能建造的政策与产业体系已基本建立,大量智能建造系统已应用于实践。基于此,从智能感知与信息解译、围岩评价及安全评估、围岩控制与动态修复、平台开发及应用推广等4个方面展望了数智化深地工程的发展方向,进而构建了基于多源信息的深地工程围岩稳定性综合评价与分析系统构想。

基于循环一致性对抗网络的地震断层训练样本合成方法研究

为了获得真实的地震断层训练样本,提出了基于循环一致性对抗网络的断层训练样本合成方法。使用随机生成的断层标签与实际断层数据作为输入,利用无监督的对抗网络学习断层标签与断层数据之间的联系,生成与断层标签特征相匹配的地震断层样本,由此得到带有标签的断层训练样本集。该方法是一种获取断层训练样本集的方法,一定程度上解决了深度学习地震断层解释缺少训练数据集的问题。对合成断层样本与真实断层进行平均主频与纹理差异的定量分析,结果表明两者具有较高的相似性。使用合成的断层样本训练神经网络,并将结果应用于实际数据测试并进行对比,结果表明合成的断层训练样本具有真实可靠的特点,所提方法可以针对不同工区生成具有目标导向性的断层,能够灵活有效地应用于不同工区的地震断层智能识别。

论人工智能透明度原则的法治化实现

随人工智能应用范围不断扩大,透明度原则成为破解“算法黑箱”难题的钥匙。透明度原则包括形式透明和实质透明。形式透明是对人工智能基础信息的披露,使人工智能的部署使用处于非秘密状态。实质透明与人工智能的可解释性息息相关,强调对披露信息以可被理解的方式进行有意义解释,从而打破知识壁垒,使相关信息实现真正可知。透明度原则应在法治化实现上进行精细考量:在考虑现有技术条件的基础上,有必要根据监管机关和社会公众的能力不同有区别地对披露和解释的信息内容提出义务要求,并针对不同人工智能的风险程度采取分级分类分场景的执行标准,从保护用户与公众知情权行使、确保国家监管权落实两方面推动透明度原则实现。

Artificial intelligence:Advances and new frontiers in medical imaging

Artificial intelligence(AI)has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago.These algorithms,ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks,have continuously sought to revolutionize medical imaging.With the number of imaging studies outpacing the amount of trained of readers,AI has been implemented to streamline workflow efficiency and provide quantitative,standardized interpretation.AI relies on massive amounts of data for its algorithms to function,and with the wide-spread adoption of Picture Archiving and Communication Systems(PACS),imaging data is accumulating rapidly.Current AI algorithms using machine-learning technology,or computer aided-detection,have been able to successfully pool this data for clinical use,although the scope of these algorithms remains narrow.Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage,however interpretation has generally remained a human responsibility for now.In this review article,we will summarize the current successes and limitations of AI in radiology,and explore the exciting prospects that deep-learning technology offers for the future.

基于改进HRNet的遥感影像冬小麦语义分割方法

冬小麦在影像中呈现田块碎小且分布零散等空间特征,同时影像包含的复杂地物对冬小麦识别造成干扰,易出现识别精度低且边界分割模糊等问题。为及时准确获取大范围冬小麦空间分布信息,该研究以高分二号卫星影像作为数据源,提出一种CAHRNet(change attention high-resolution Net)语义分割模型。采用HRNet(high-resolution Net)替换ResNet作为模型的主干网络,网络的并行交互方式易获取高分辨率的特征信息;联合OCR(object-contextual representations)模块聚合上下文信息,以增强像素点与目标对象区域的关联性;3)引入坐标注意力(coordinate attention)机制,使网络模型充分利用有效的空间位置信息,以保留分割区域的边缘细节,提高对分布零散、形状多变的冬小麦田块的特征提取能力。试验结果表明,在自制的高分辨率遥感数据集上,CAHRNet模型的平均交并比(mean intersection over union,MIoU)和像素准确率(pixel accuracy, PA)分别达到81.72%和97.08%,MIoU相较U-Net、DeepLabv3+分别提高了9.09、2.44个百分点;PA相较U-Net、DeepLabv3+分别提高6.80、1.59个百分点,说明CAHRNet模型具有较高的分割识别精度,可为进一步准确获取冬小麦作物分布信息提供技术支撑。

基于深度学习SSD算法的高密度电法智能解译方法技术研究

高密度电法在探测灰岩区地下溶洞病害体方面得到广泛应用,但高密度电法反演结果依赖于初始模型,存在多解性,地质解译容易受专业人员主观因素影响。为此,本文从具有唯一性的视电阻率数据出发,研究了基于深度学习的SSD(Single Shot Multi-box Detector)目标检测算法的视电阻率异常智能解译方法技术。针对岩溶地质病害,设计了不同填充类型、形状、规模、数量的溶洞电性异常模型,利用Res2dmod软件进行视电阻率正演计算,构建了包含1400个样本的高密度电法视电阻率智能解译学习样本库(样本和标签)。基于TensorFlow框架,建立了基于深度学习SSD算法的高密度电法视电阻率异常智能解译方法技术,使用学习样本库训练网络权值,训练结束后对高密电法温纳装置视电阻率异常进行智能解译,单个视电阻率剖面异常智能解译耗时不到1 s,各类目标(填充型溶洞、未填充型溶洞)平均准确率为90.68%。研究结果表明:基于SSD算法的高密度电法视电阻率异常智能解译技术可显著提高高密度电法视电阻率解译效率,避免专业人员主观因素影响。

新型电力系统中的大模型驱动技术:现状、机遇与挑战

建设新型电力系统是实现社会、经济高质量发展的重要基石,以新一代通用人工智能为主导的信息化技术与能源电力科学深度耦合,为新型电力系统的数字化转型工作提供了重要保障。为了探究电力系统在大模型时代潮流下的发展方向,该文首先系统梳理了当前电力系统的数智化发展现状以及在新的场景需求下遇到的难点问题。然后重点探讨了以多模态大模型为代表的具备深度场景解析和语言描述能力的大模型技术在电力系统中的应用前景,并分析了其在几个典型场景下的相关应用成果,证明大模型技术可行性的同时,进一步总结了大模型技术在相关电力场景所面临的机遇与挑战性问题。最后对未来大模型技术如何与电力系统实现紧密融合做了展望与建议。该研究成果有望为新型电力系统数字化转型过程中的数智化发展提供参考,助力能源电力领域提质增效。

矿物组分识别与智能解释在不同岩性之间的信息共享与迁移学习

在地球科学领域,岩石微观观测数据的采集过程繁琐且效率低下,这不仅增加了研究成本,降低了可靠性,同时也限制了数据的开源共享。此外,由于岩性的多样性和观测手段的差异,单一数据集的规模通常较小,这对于依赖大规模数据集的深度学习框架而言是一大挑战。为此,本研究探索迁移学习如何促进不同岩性间的信息共享,并通过此机制提高矿物组分识别与智能解释任务的模型性能。通过采集不同区域、岩性、矿物组分和偏光模式下的铸体薄片样本,本文深入研究了深度学习模型在不同观测对象和手段下的迁移学习机制,并聚焦于探索地质信息的深层表征。研究成果不但揭示了迁移学习在促进地质学领域信息共享与模型性能提升中的关键作用,还为自动化和智能化地质认识融合奠定了基础。实验结果显示,通过迁移学习,本文模型在智能解释任务中的准确率显著提高,从53.3%提高至98.73%,而在矿物组分识别任务中,准确率也实现了近10%的提升。这些成果证明了迁移学习在地质学领域内解决实际问题和提高模型泛化能力、性能和稳定性方面的巨大潜力。

可解释机器学习在油气领域人工智能中的研究进展与应用展望

人工智能作为战略性新兴产业及新质生产力正迅速地渗透入油气领域,并有望成为行业发展的新引擎和制高点。“黑盒”的机器学习模型缺乏透明度和可解释性,导致现有机器学习方法在油气领域的认可度和信任度不高,制约了以机器学习为核心的人工智能在油气田中的融合和发展。为此,系统介绍了可解释机器学习方法在油气田勘探开发过程的研究现状,阐述了机器学习模型的可解释性是促进油气领域人工智能大规模应用的关键,以及事后可解释方法在油气机器学习方法上的局限性,并对技术的应用进行了展望。研究结果表明:(1)利用Shapley加性解释(SHAP)和模型无关局部解释(LIME)等事后可解释方法进行煤层气产能主控因素实例验证,指出了可解释的油气田特征指标还不足以完全指导可解释模型的构建和分析,需要基于本质可解释思路建立符合油气田勘探开发自身特点的本质可解释机器学习方法;(2)利用机理模型、因果推断和反事实解释等本质可解释方法,分析油气田数据和模型参数之间的因果关系,构建了本质可解释机器学习方法;(3)选取典型煤层气压裂数据进行产能预测实例验证,发现因果推断能有效挖掘地质参数、施工参数和产能之间的本质关系,且基于因果关系建立的机器学习模型可以实现预测泛化性能提升。结论认为,基于事后可解释和本质可解释机器学习方法不仅是未来油气领域人工智能发展的必然趋势,而且是解决人工智能在油气领域现场落地的“瓶颈”问题及关键技术。

智能物探技术的过去、现在与未来

通过梳理国内外人工智能技术在地球物理勘探(物探)领域中的发展历程、主要研究进展以及发展方向,总结了智能物探的优势和面临的难题,并提出了解决方案。研究结果表明:(1)物探技术在人工智能发展的第2次浪潮中开始与人工智能技术相结合,得益于物探领域数据量的指数级增长、硬件算力的高速发展以及不断出现的新深度学习框架,智能物探技术从早期的机器学习发展为目前的深度学习,在地震资料处理、解释等方面的应用中取得了大量研究成果。(2)目前智能物探技术被广泛应用于标签集的构建、去噪、断裂检测、层位与层序解释、地震相分类和异常体检测、岩性识别与油气藏开发、地震反演成像等方面,大幅提高了工作效率,降低了工作成本,克服了人工交互操作和人工经验的主观性和不可靠性,助力打破传统物探技术瓶颈。(3)智能物探技术的发展面临着缺少公开的标签数据集、缺少解决地球物理领域问题的智能化框架及尚未形成适用于地球物理领域共享的智能化开发平台等难题,可以从解决数据基础、构建智能平台、开展网络架构基础性研究及与应用场景结合等方面着手解决;此外,智能物探技术的发展方向还包含智能地震成像方法研究,储层成像方法研究,油气大数据挖掘、智能风险评估与智能决策以及超算软件装备研发等方面。

《人工智能辅助宫颈细胞学诊断技术的应用及质量控制专家共识》解读

2023年12月中国妇幼保健协会和中国妇幼保健协会妇女病防治专业委员会共同发布了《人工智能辅助宫颈细胞学诊断技术的应用及质量控制专家共识》(以下简称《共识》)。本文旨在对《共识》进行解读,便于相关专业人员在工作中进一步理解和实践。

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视频侦查技术的核心是通过视频图像的提取、查看、分析和研判来获取侦查线索、固定涉案证据。多年来经过在实战应用中不断地发展创新,视频侦查技术形成了自己独特的关键技术体系:视频信息分析解读技术是侦查应用和证据固定的基础和核心;视频证据固定保全技术的规范是审判中心主义的客观要求,可以获取视频侦查记录报告、视频检验鉴定报告或视频数据关联报告;低质量视频图像的增强恢复技术专业性强,应用范围窄,技术成熟度高,然而它不断面临新的挑战。目前,视频数据的智能应用在大数据背景下变得越来越重要,仍需进一步突破视频自动识别技术的应用范畴,建立完善多层次的视频数据综合应用体系,打造适应不同业务需要的视频数据实战应用模型。