Analysis of coseismic effect on temperature in the Three Gorges well network

Through the Three Gorges well network, we examine different coseismic changes in water temperature caused by local earthquakes since 2008, and offer a mechanistic explanation.The relations between the coseismic changes in water temperature and the parameters of distant and local earthquakes are deduced.

Coseismic response of groundwater level in the Three Gorges well network and its relationship to aquifer parameters

The 12 May 2008 Ms 8.0 Wenchuan earthquake caused notable changes in the water levels of wells in the Three Gorges area. This work examines the relationship between these coseismic changes in water level and the changes in aquifer parameters. Three wells in the area with good responses to earth tide were chosen for analysis. Water-level data from February to June 2008 were used to calculate the aquifer transmissivity, permeability and specific storage of the rocks, and analyze the relationship between the coseismic responses of the wells and their aquifer parameters. The results show that the Wenchuan earthquake changed these parameters considerably, thereby controlling co- and postseismic variations of water level. The values of these parameters prior to the earthquake are linearly related with the amplitudes of coseismic variations in water level. The larger the aquifer transmissivity, the more remarkable the coseismic change in water level. During the earthquake, changes in aquifer parameters were found to be associated with coseismic variations in water level, with the larger changes occurring when the coseismic variation in water level was larger. The tectonic setting has a certain degree of influence on the co- and postseismic changes in water level. The permeability structures of the fault zone appear to determine the manner of coseismic variation in water level. Moreover, it seems that the water level in wells where groundwater converges more easily can recover faster following an earthquake. Insight from this study helps to improve understanding of the characteristics of water-level changes caused by earthquakes.

Coseismic responses of groundwater levels in the Three Gorges well-network to the Wenchuan M_S8.0 earthquake

We systematically analyze coseismic responses and post-seismic characteristics of groundwater levels in the Three Gorges well-network to the Ms8.0 Wenchuan earthquake on 12 May 2008. The results indicate that these characteristics differ among wells. On the conditions of similar borehole configurations, the differences are associated with geological structural sites of wells, burial types of aquifers monitored, and transmissivities of aquifer systems. We explored coseismic and post-seismic step-rise and step-drop mechanical mechanisms and their implication to earthquake prediction. We validated the inference that the residual step-rise zone is a possible earthquake risk zone based on recent seismic activity on the Xiannüshan fault in the area.

Research of Energy-saving Control of Oil-well Power Heater Based on RNN Neural Network

For the beam pumping unit,the power consumption of oil-well power heater accounts for a large part of the pumping unit.Decreasing the energy consumption of the power heater is an important approach to reduce that of the pumping unit.To decrease the energy consumption of oil-well power heater,the proper control method is needed.Based on summarizing the existing control method of power heater,a control method of oil-well power heater of beam pumping unit based on RNN neural network is proposed.The method is forecasting the polished rod load of the beam pumping unit through RNN neural network and using the polished rod load for real-time closed-loop control of the power heater,which adjusts average output power,so as to decrease the power consumption.The experimental data show that the control method is entirely feasible.It not only ensures the oil production,but also improves the energy-saving effect of the pumping unit.

A Scalable Model of the Substrate Network in Deep n-Well RF MOSFETs with Multiple Fingers

A novel scalable model of substrate components for deep n-well (DNW) RF MOSFETs with different number of fingers is presented for the first time. The test structure developed in [1] is employed to directly access the characteristics of the substrate to extract the different substrate components. A methodology is developed to directly extract the parameters for the substrate network from the measured data. By using the measured two-port data of a set of nMOSFETs with different number of fingers, with the DNW in grounded and float configuration, respectively, the parameters of the scalable substrate model are obtained. The method and the substrate model are further verified and validated by matching the measured and simulated output admittances. Excellent agreement up to 40 GHz for configurations in common-source has been achieved.

Intelligent Recognition Method of Insufficient Fluid Supply of Oil Well Based on Convolutional Neural Network

Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing;then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network;finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site.

Synthetic well logs generation via Recurrent Neural Networks

To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.

4D-stress evolution of tight sandstone reservoir during horizontal wells injection and production: A case study of Yuan 284 block,Ordos Basin,NW China

To investigate the 4D stress change during injection and production in tight sandstone reservoirs, a multi-physical fields modeling method is proposed considering the reservoir heterogeneity, hydraulic fracture and complex injection-production system. The 4D stress evolution of tight sandstone reservoir in Yuan 284 block of Huaqing oilfield, Ordos Basin,during injection-production in horizontal well network is investigated by modeling coupled flow and geomechanics. Results show:(1) Induced by injection and production, the 3D stress increases near the injectors but decreases near the producers, and the horizontal stresses are distributed in obvious strips along their respective stress directions.(2) The horizontal stress difference is the highest at the horizontal wellbore beside injectors during injection and production, while it is the lowest in undeveloped zone between the injectors, and the orientation of maximum horizontal principal stress changes the most near the injectors, which is distributed radially.(3) The hydraulic fracture in re-fracturing well was observed to be asymmetrical in geometry and deflected as the stress changed. The results provide theoretical guidance for horizantal well network modification and re-fracturing optimization design in tight sandstone reservoir.

基于Wells评分和网络宣教的分级护理预防腹腔镜胆囊切除术后静脉血栓栓塞症的临床研究

目的:探讨基于Wells评分和网络宣教的分级护理预防腹腔镜胆囊切除术后静脉血栓栓塞症的效果。方法:选取2014年10月-2018年12月于济南市中医医院与利津县明集中心卫生院进行腹腔镜胆囊切除术(laparoscopic cholecystectomy,LC)治疗的患者186例,按护理方法不同分为对照组(n=90)与观察组(n=96)。对照组给予常规护理,观察组给予基于Wells评分和网络宣教的分级护理。比较两组术后Wells评分分级、VTE发生情况及对VTE相关知识的掌握情况。结果:对照组高危险度者比例高于观察组(P<0.05)。观察组VTE发生率为1.04%低于对照组的6.67%(P<0.05)。观察组VTE相关知识掌握评分高于对照组(P<0.05)。结论:基于Wells评分和网络宣教的分级护理措施能够有效提高患者及家属对VTE危害和预防方法相关知识的掌握度,降低LC术后患者VTE发病风险。

Coseismic effects recorded by Fujian subsurface fluid network and its meaning to earthquake prediction

Based on analyses of the spatio-temporal evolutionary characteristics of teleseismic response recorded by Fujian subsurface fluid network and in combination with earthquakes happened in Fujian province during the same period, this paper points out that the step-like rising of water level after distant earthquakes may include some regional stress field information, and the area where water level step-like rises could be the position that the stress concentrated on and where the future earthquakes would occur. If combined with other impending precursors, the location of the events may be predicted to a certain degree.

基于神经网络模型的水平井破裂压力预测方法

破裂压力是井身结构设计的基础依据,也是水力压裂设备选型和方案设计的基础参数,通常采用测井解释获取破裂压力剖面,但其存在参数准确获取难、计算过程繁琐、普适性较差、计算精度低等问题,机器学习提供了一种解决这些问题的新方法。为此,以测井数据作为输入参数,采用4种不同的神经网络模型,建立水平井测井数据与破裂压力间的非线性关系,通过测试集预测结果的对比分析,优选出最佳的神经网络模型,并优化模型网络结构和超参数,实现水平井破裂压力的直接预测。研究结果表明:1)破裂压力与井斜角、横波时差和纵波时差表现为极强相关性,与井深、岩性密度和补偿中子表现为强相关性,与井径和自然伽马表现为弱相关性;2)不同组合的测井参数对模型预测结果具有显著影响,最优输入参数为井斜角、横波时差、纵波时差、井深、岩性密度和补偿中子;3)对比多层感知机、深度神经网络、循环神经网络和长短期记忆神经网络(LSTM)模型,发现LSTM模型的预测效果最佳;4)优化了LSTM模型的网络结构及超参数,优化后破裂压力预测的平均绝对百分比误差为0.106%、决定系数为0.996。LSTM模型能够有效构建水平井测井参数与破裂压力之间的非线性关系,可以实现水平井破裂压力的准确预测,对于准确预测破裂压力、简化破裂压力计算过程、推广机器学习在石油工程领域的应用具有重要的作用。

A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.

致密灰岩水平井复合缝网加砂压裂技术研究与矿场实践

鄂尔多斯盆地太原组石灰岩资源丰富,是长庆油田天然气勘探开发的重要接替领域。但由于储层致密、厚度薄、酸岩反应速率快等因素,以往多种酸压工艺均未能获得产量突破。为此,转变增产技术思路,强化裂缝扩展规律实验研究、压裂液及关键材料研发配套、体积压裂模式精细刻画,形成“多段少簇密集造缝、酸压—加砂双元复合”为一体的水平井复合缝网加砂压裂技术。通过开展大露头压裂物理模拟实验,结合储层地质特点及岩石力学特征,明确储层高脆性、低两向水平主应力差、微裂缝发育特征,采用体积压裂工艺能够实现复杂缝网改造;集“深度酸压+大规模加砂”双重技术优势,构建“水力缝网+酸蚀裂缝”相结合的高导流裂缝流动通道,结合水平井密切割压裂大幅提高裂缝改造体积;针对石灰岩高杨氏模量、高破裂压力、高裂缝延伸压力、低裂缝宽度的三高一低特征,通过提高套管承压等级、差异化裂缝铺砂设计,形成多尺度小粒径连续加砂模式,解决了高杨氏模量储层加砂难问题;基于基质、壁面、裂缝三级伤害评价,研发低伤害变黏滑溜水体系,实现高杨氏模量储层造缝、成网、携砂需求。现场试验4口井,平均单井产量为59.7×10^(4)m^(3)/d,较酸压直井提高5~20倍以上,增产效果显著。目前,水平井复合缝网加砂压裂技术已成为太原组石灰岩水平井储层改造的主体技术,为该类气藏的勘探突破及有效开发提供了有力的技术保障。

稀疏井网下隔夹层精细预测方法研究及效果分析

在薄层预测领域,地质统计学和波形指示反演技术是当前的两大主流方法。然而,地质统计学反演在井点稀疏区域构建精确变差函数方面存在挑战,难以实现对薄层的准确预测。尽管波形指示反演对井网分布的依赖性较小,但其高频成分在描述地震振幅的空间变化方面存在不足。为了克服这些限制,提出了叠前构形反演技术。该技术通过对比地震数据体中能量和频率特征的横向变化,有效预测目标岩性的空间分布。充分利用了地震数据体的横向信息,其高频信息与地震数据体的空间能量变化高度吻合。通过射线域弹性阻抗分析,该方法能够提取更多弹性参数,其结果与AVO特征相一致,从而更准确地反映叠前地震数据不同射线域能量的横向变化,精确预测多种岩性类型。在稀疏井网的研究区,对于复杂岩性隔夹层的预测具有显著优势。该方法在多个油田的实际应用中已显示出卓越的效果,对于1 m以上夹层的预测准确率超过80%,明显优于传统的反演技术,特别适合于稀疏井网区域的复杂岩性隔夹层预测。

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

Cyclic pressure pulsing with nitrogen is studied for hydraulically fractured wells in depleted reservoirs.A compositional simulation model is constructed to represent the hydraulic fractures through local-grid refinement.The process is analyzed from both operational and reservoir/hydraulic-fracture perspectives.Key sensitivity parameters for the operational component are chosen as the injection rate,lengths of injection and soaking periods and the economic rate limit to shut-in the well.For the reservoir/hydraulic fracturing components,reservoir permeability,hydraulic fracture permeability,effective thickness and half-length are used.These parameters are varied at five levels.A full-factorial experimental design is utilized to run 1250 cases.The study shows that within the ranges studied,the gas-injection process is applied successfully for a 20-year project period with net present values based on the incremental recoveries greater than zero.It is observed that the cycle rate limit,injection and soaking periods must be optimized to maximize the efficiency.The simulation results are used to develop a neural network based proxy model that can be used as a screening tool for the process.The proxy model is validated with blind-cases with a correlation coefficient of 0.96.

基于工业物联网的地热井阻垢剂加注装置研制

为解决西藏地区高温地热井结垢问题,该文研制了一套基于工业物联网的阻垢剂加注装置,设计了五层架构:现场层、控制层、操作层、管理层、企业层。现场层、控制层、操作层为本地系统,基于S7-1200、触摸屏、WinCC界面实现阻垢剂加注现场监控,并通过西门子工业网关(IoT 2050)向云服务器传输加注压力、流量、深度等生产数据。管理层、企业层为远程系统,通过生产执行系统(MES)监控多个地热井的阻垢剂加注装置,进行多井联产管理。该装置在西藏谷露某地热井进行现场测试,阻垢剂加注流量调节范围可达0~60 L/h,压力0~2.5 MPa,深度0~300 m,可以满足实际需要。

基于前馈神经网络井控多属性融合的断裂识别方法

塔里木盆地碳酸盐岩断控缝洞型油气藏埋藏深度大、构造复杂,且断裂高度发育,断裂是研究区域内成藏主控因素及可能的油气运移通道,对其空间展布位置及发育强弱的预测至关重要。断裂检测属性众多,不同断裂检测属性由于计算方法不同表征的断裂尺度及特征存在一定的差异性,且常规属性检测忽视了测井信息的利用与约束,为了获取更加全面、准确的断裂预测结果,本文优选多类断裂检测属性,并结合测井数据作为先验信息,利用前馈神经网络算法进行属性融合。首先优选AFE、likelihood、倾角等多类具有不同特征的断裂属性,结合测井放空漏失数据、成像测井信息及地震同相轴错段情况作为断裂发育类型判别条件建立了断裂特征识别样本库;在样本库基础上进行深度前馈神经网络训练,对比测试了不同隐含层深度条件下的学习效果,获取预测误差最小的神经网络预测模型;最后将神经网络预测模型应用于全工区断裂预测。经对比分析,认为深度学习融合属性预测断裂与测井解释结果更为吻合,且能综合不同尺度特征的断裂信息,有效提升了预测准确度和可靠性。

多分支废弃油井层温衰减规律及注入参数对注采性能的影响

利用废弃油井开发地热资源是降低地热开发成本、促进商业推广的有效措施,其中层温衰减规律对地热系统取热效果具有重要影响。为此,以废弃油井储层为研究对象,结合泰森多边形理论对储层裂缝进行了划分,建立了三维油水两相热流耦合数值模型,分析了不同储层条件与布井结构下的层温衰减规律,探究了注入参数等因素对地热注采性能的影响。研究结果表明:(1)低渗透储层抽采30年生产温度最大差值可达22.08 K,高渗透储层(1.0×10^(-13) m^(2))温度过度衰减,长年限采热效率低;(2)储层含油率与生产温度和发电功率呈正相关关系,生产30年后差值最大可达11.57 K与0.12 MW;(3)分支井沿垂直方向会形成低温波谷,层温衰减高于储层其余部分,数量增加对注采影响小;(4)注入速率与发电功率、生产温度分别呈正、负相关关系,但注入速率超过89 kg/s时不利于长年限开采。结论认为:(1)储层取热性能对初始渗透率最为敏感,注入速率与储层含油率次之,优先开发低渗透储层有利于长年限采热;(2)应利用油田地热降低稠油黏度,形成地热辅助采油模式,以实现经济效应最大化;(3)选取6支分支井与89 kg/s的注入速率可得到可观取热效果和较好的经济效益。

一种页岩气井的缝网沟通诊断方法

随着对页岩气藏地质认识的深化,页岩气藏立体开发调整已是提高页岩气储量动用率和采收率的一种常用方式。受井距小、天然裂缝发育、老井动用不均等影响,页岩气立体开发井之间的压裂干扰不可避免且干扰类型多样,老井表现出的动态特征差异明显,故以生产响应为基础的缝网沟通诊断必不可少。文中以南川工区页岩气藏为研究对象,采用水力压裂模拟、气藏工程动态分析等手段,开展了页岩气井缝网沟通诊断方法研究。结果表明:1)按照裂缝导流能力,立体开发井缝网沟通机理可表现为天然裂缝与压裂缝所组成缝网的4种沟通形式。2)响应于不同的缝网沟通形式,在立体开发井压裂施工期间,老井关井套压呈现平稳上涨、上涨波动、轻微波动回落、剧烈波动等4种波动类型,套压涨幅主控因素为天然裂缝发育程度、井间采出程度及新井强改造规模等。3)结合老井复产后生产指标、递减规律、试井曲线等的变化特征,建立了南川工区页岩气立体开发井压裂干扰缝网沟通预测表。在矿场实践中,可以根据预测表中的某几个特征,预测井间生产干扰或缝网沟通情况,为精细化数值模拟和立体开发井分段压裂优化提供科学指导。

耦合生态系统服务供需的生态安全格局构建——以苏南地区为例

生态系统服务供需平衡是保障生态安全的基础,将生态系统服务供需作用流动机制融入生态安全格局构建对保障区域生态安全和提升人类生态福祉具有重要意义。基于景观生态学原理,提出耦合生态系统服务供需的生态安全格局构建逻辑方法,以苏南地区为研究区构建了景观生态安全格局和社会生态安全格局。研究结果表明:(1)耦合生态系统服务供需的生态安全格局应以维系区域生态安全和保障区域人类生态福祉为构建目标。其中,生态安全以生态系统服务与生态风险为核心关注对象,人类生态福祉的实现依赖于人类主动获取与生态系统服务流;(2)苏南地区生态系统服务供需间存在错配格局,在此基础上分别识别生态源地与需求源地4247.46km^(2)、1882.16km^(2),生态廊道与供需廊道1614.02km、1915.82km,生态夹点29处、生态障碍点23处、生态供需节点20处;(3)形成“三区四带两组团”的社会-景观生态安全格局优化布局方案,并在此基础上提出针对性保护修复策略,推动经济-生态空间协同发展。研究可为丰富区域生态安全格局构建理论和方法、推动国土空间优化与管控提供参考借鉴。