Diagnostic Method and Adaptability Analysis of Multiple Water Breakthroughs in Horizontal Well in Combined Well Pattern

The combined well pattern has been widely used in reservoir development, with a common pattern being a horizontal well in the center for oil production and multiple vertical wells around for water injection. For this type of well pattern, determining the horizontal well is affected by which injection wells, especially when the injecting water breaks through, accurately determining the direction of water inflow will provide an important basis for targeted water well measures. Based on the production performance data of horizontal wells, the semi logarithmic relationship curves of water-oil ratio, derivative water-oil ratio, and cumulative production were used for the first time to determine the breakthrough problem of water injection in the surrounding water injection wells of horizontal wells based on their response characteristics. The adaptability of this method under different influencing factors was analyzed. Introducing the parameter of cumulative production not only preserves the variation trend of the derivative of water-oil ratio with time, but also facilitates the processing of actual production data.

A novel method for predicting breakthrough time of horizontal wells in bottom water reservoirs

Dimensional analysis and numerical simulations were carried out to research prediction method of breakthrough time of horizontal wells in bottom water reservoir. Four dimensionless independent variables and dimensionless time were derived from 10 influencing factors of the problem by using dimensional analysis. Simulations of horizontal well in reservoir with bottom water were run to find the prediction correlation. A general and concise functional relationship for predicting breakthrough time was established based on simulation results and theoretical analysis. The breakthrough time of one conceptual model predicted by the correlation is very close to the result by Eclipse with less than 2% error. The practical breakthrough time of one well in Helder oilfield is 10 d, and the predicted results by the method is 11.2 d, which is more accurate than the analytical result. Case study indicates that the method could predict breakthrough time of horizontal well under different reservoir conditions accurately. For its university and ease of use, the method is suitable for quick prediction of breakthrough time.

Self-sealing of fractures in indurated claystones measured by water and gas flow

Self-sealing of fractures in the indurated Callovo-Oxfordian(COX)and Opalinus(OPA)claystones,which are considered as host rocks for disposal of radioactive waste,was investigated on artificially fractured samples.The samples were extracted from four lithological facies relatively rich in clay mineral,carbonate and quartz,respectively.The self-sealing of fractures was measured by fracture closure,water permeability variation,gas penetration,and recovery of gas-induced pathways.Most of the fractured samples exhibited a dramatic reduction inwater permeability to low levels that is close to that of intact rock,depending on their mineralogical composition,fracture intensity,confining stress,and load duration.The self-sealing capacity of the clay-rich samples is higher than that of the carbonate-rich and sandy ones.Significant effects of sample size and fracture intensity were identified.The sealed fractures become gas-tight for certain in-jection pressures.However,the measured gas breakthrough pressures are still lower than the confining stresses.The gas-induced pathways can recover when contacting water.These important findings imply that fractures in such indurated claystones can effectively recover to hinder water transport but allow gas release under relatively low pressures without compromising the rock integrity.

Adsorption Breakthrough of Benzene in the Fixed Bed of Modified Activated Carbon under Different Humidity Conditions

The breakthrough curves of benzene and water on modified activated carbons（ACs） were investigated.Temperature-programmed desorption（TPD） experiments were conducted to measure the TPD curves of benzene and water on modified and unmodified ACs and to estimate the activation energy for the desorption of benzene on the modified ACs.Starting with unmodified ACs,two modified ACs were prepared by using two different types of silane,designated by KH560 and 1706.The results showed that the activation energy for the desorption of benzene on KH560/AC and 1706/AC was higher than that on unmodified AC.In addition,the activation energy for the desorption of water on KH560/AC and 1706/AC was lower than that on unmodified AC.The breakthrough curves of benzene obtained from the experimental observations under different humidity conditions were compared with the results of the TPD experiments.The results show that the modified ACs are less affected by water,whereas the unmodified ACs are more affected by water,indicating that surface modification by organosilane compounds can improve the adsorption of benzene on the activated carbo,which weakens the adsorption of water.

Design of Pilot Plant Packed Column for the Dehydration of Water from Ethanol-Water Mixtures

This use of biomass-based adsorbent has been explored for the column study of the adsorptive dehydration of water in ethanol-water mixtures. The column study was carried out using enzyme modified corn starch and the breakthrough curve parameters were used to design the packed bed column. The effect of flow rate on the breakthrough curves revealed that adsorption efficiency decreased with increased inflow rate. The empty bed contact time (τ) of the pilot plant packed column was 35.35 min while the breakthrough time is 40.78 min. 66.7% was the fraction of capacity left unused for the pilot plant from the design.

Effect of Polyacrylamide Application on Water and Nutrient Movements in Soils

An understanding of nutrient movement in soil is important for developing management strategies to minimize nutrient leaching and surface movement, thus improving nutrient uptake by plants, maintaining a sustainable soil system, and even protecting groundwater quality. Polyacrylamide (PAM) is known as one of soil conditioner that functions to stabilize soil structure, increase infiltration, and reduce surface runoff. This study assesses the effects of PAM on the vertical movement of soil-water and major/minor nutrients (NO3-N, NH3-N, T-N, PO4-P, T-P, K, Ca, Mg, and Fe) in soils. Saturated hydraulic conductivity (Ksat) increases with increasing PAM concen- trations up to 10 mg·L-1 for sand and 20 mg·L-1 for a mixture of sand and clay. Decreases in the loss of soluble nutrients, particularly NH3-N, PO4-P and T-P, are observed as PAM concentrations increase. In contrast, PAM concentration has no effect on nitrate and minor nutrient levels. These results indicate that the application of PAM may be a viable method for protecting water bodies from excessive nutrients and improving nutrient availability for plants.

Analysis of solute preferential transport in a dark coniferous forest ecosystem of Gongga Mountain,Sichuan Province,southwestern China

We selected a dark coniferous forest ecosystem of Gongga Mountain in the upper reaches of the Yangtze River as our research area to study the preferential flow and solute preferential transport by adding the tracers KNO3 and KBr to the self-made soil column equipment in different ways to examine density and volume changes of inflows and outflows of a mass input （impulse input） and a stable, well-distributed input （step input））. The results showed that this dark coniferous forest ecosystem of Gongga Mountain is a typical area of preferential flow and solute preferential transport, a process that can be classified into five parts. A great amount of solute was transported at high speed as the result of preferential flow in the soil and caused the density of the solute in both deep water and in groundwater to rise rapidly, which definitely increased pollution in the deep soil layer.

Importance of Water Chemistry in Oil and Gas Operations—Properties and Composition

In the oil and gas industry, water is essential element since it exists in most of the stages, starting from drilling operations, then production processes including injecting in the reservoir to maintain the pressure, and finally disposing the produced water from the reservoir. Therefore, it is very important to understand the water in the petroleum industry with its different types and study its effects on the reservoirs as well as the downhole and surface equipment. Most of the studies and literature reviews focused on the oil and gas properties. In fact, understanding the properties of the water is as important as the oil and gas properties. Water is always present in the oil and gas fields during production, injection or disposal. Therefore, it has a direct impact on the productivity and overall efficiency of the field and the entire development project. This paper is going to illustrate and focus on several objectives. First, it elaborates the importance of the water in the oil and gas fields. Then, it includes a comprehensive overview of different types of water in the petroleum industry including a summary about of their properties. The main two types that the paper focuses on are the produced water and the injected water. It clarifies the effects of both of them on the reservoir, well completions and facilities. After that, the paper touches bases on the common problems and issues caused by water along with a brief summary of steps needed by the operators to follow in order to solve them. In addition to that, the common monitoring procedures and tests that are usually followed to observe the quality of the water are going to be presented. Finally, couples of solutions used by operators are reviewed to solve the problem of excessive water production after the water breakthrough in the field.

Batch and fixed-bed column studies of selenite removal from contaminated water by orange peel-based sorbent

Orange peel is a biomass derived from citrus processing with desirable properties for metal sorption.In recent years,orange peel has been used to remove various heavy metals and toxic oxyanions.Selenium(Se)is an essential trace element for mammals.However,when the concentration of selenium exceeds an umbral limit,it becomes toxic.In this study,orange peel was used to treat Se(IV)-contaminated water.A high sorption capacity of 32.5 mg/g was obtained at a temperature of 20℃and a pH of 2.0.Hydroxyl groups bound Se(IV)to the surface of the orange peel.The sorption process was spontaneous and endothermic.A chemical sorption mechanism was involved in the removal of Se(IV).The Thomas and modified dose-response models were used to simulate the experimental breakthrough curves.The bed depth service time model was used to calculate the critical bed depth(Z0),and the calculated Z0 value was 1.6 cm.This study reveals that orange peel is a useful sorbent for Se(IV),and can be used for the purification of Se(IV)-contaminated water.©2020 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

跨界知识搜寻、吸收能力对制造企业突破式创新的影响研究

基于创新理论、资源基础观理论与组织学习理论,构建跨界知识搜寻、吸收能力、战略柔性与企业突破式创新的理论模型和分析框架,运用SPSS和SmartPLS软件进行实证检验。研究结果表明:技术知识跨界搜寻正向影响企业突破式创新;市场知识跨界搜寻对企业突破式创新没有显著影响;知识跨界搜寻正向影响企业吸收能力;吸收能力正向影响企业突破式创新;吸收能力在跨界知识搜寻与企业突破式创新之间发挥显著中介作用;战略柔性在跨界知识搜寻对企业突破式创新的影响中发挥显著调节作用。

基于LIF对水包油型轻质油乳化液的分类识别

海面溢油是海洋污染的重要形式之一,溢油在风化迁移过程中,形成如水包油,油包水,水包油包水等乳化液,其中水包油类型的乳化液受水分子的影响较大,荧光特性不突出,导致轻质油乳化液分类的困难,因此如何进行高效识别,对污染治理有重要意义。选取常见的几种轻质油分别与海水、乳化剂按照不同配比混合配置水包油类型的轻质油乳化液。采用实验室搭建的便捷激光诱导荧光(LIF)系统探测轻质油乳化液的荧光光谱。构建麻雀搜索算法(SSA)优化支持向量机(SVM)的分类模型(以下简称SSA-SVM),实现乳化阶段溢油的分类识别。采用主成分分析法(PCA)对荧光光谱进行降维,选取累计贡献率为99%的前三个主成分作为输入,轻质油的种类作为输出;采用SSA迭代得到SVM的最优参数,并构建SSA-SVM分类模型;最后将测试集样本代入到模型中进行分类识别,识别准确率为100%。该研究同时构建了粒子群算法(PSO)优化支持向量机模型(简称PSO-SVM)和遗传算法(GA)优化支持向量机模型(简称GA-SVM)作为对照,实验结果表明,SSA-SVM相比PSO-SVM和GA-SVM,测试集的轻质油乳化液分类识别准确率同比提升1.77%和3.04%;并且适应度曲线在第2代就达到最高,优于PSO的第4代和GA的第36代,收敛速度更快。采用激光诱导荧光技术实现了水包油类型的轻质油乳化液的分类识别,推进了海面溢油区域分类探测机理的发展;提出的SSA-SVM模型,为轻质油乳化液的分类识别研究提供了新思路。

基于BP神经网络的低渗透底水油藏油井见水模式预测模型

注水开发使得低渗透底水油藏油井见水模式更加复杂,需要进一步明确及预测油井见水模式来针对性地指导水淹治理措施。神经网络模型具备处理多元回归问题和计算速度快等优势,可被用于分析地质工程多因素参数与油井见水模式的内在关系,构建见水模式预测模型。在油井见水模式划分的基础上,通过灰色关联理论和神经网络算法对BCL低渗透底水油藏油井见水模式的主控因素和预测模型进行了研究。发现水层厚度、隔夹层数、隔夹层长度和避水高度是该类油藏注水开发下影响油井见水模式的主控因素。基于主控因素结合神经网络算法建立了油井见水模式预测模型。通过对18组测试数据进行验证,平均预测误差1.4%,获得了较好的预测精度。通过易于获取的主控因素快速预测注水开发低渗透底水油藏油井的见水模式,为该类油藏的高含水针对性治理提供基础依据。

时空异质性视角下长江经济带水资源系统韧性评估及调控因子分析

水资源系统韧性的评估与调控是缓解水资源危机和规避水资源风险的重要途径之一。从水资源的自然资源、社会经济、生态环境等属性出发,综合考虑水资源系统韧性的抵抗性、恢复性、适应性特征,构建了包含水资源、水经济、水福利、水管理、水生态和水环境6个维度的水资源系统韧性评价指标体系,建立了基于麻雀搜索算法的投影寻踪模型,运用自然断点法识别区域空间分异特征,采用因子探测识别影响水资源系统韧性空间分布格局的调控因子。通过对2010—2019年长江经济带11个省(市)水资源系统韧性的分析发现,在空间上,长江经济带水资源系统韧性表现为西部最优、东部次之、中部最末,且南部优于北部;在时间上,长江经济带水资源系统韧性虽有波动但整体呈现上升的趋势。从6个维度来看,水生态韧性维度的韧性值区间跨度较大,在0.08~0.52之间波动,水福利韧性最高,水环境韧性次之。水资源和水福利的相关因子对水资源系统韧性的影响较为明显,水经济中的工业废水治理投资占GDP的比重和万元工业增加值用水量2个指标以及水福利中的供水管道密度和人均供水量2个指标的影响力逐渐上升。通过对长江经济带水资源系统韧性和调控因子的分析,有针对性地提出了提高长江经济带水资源系统韧性的对策建议。

海洋环境下深水区立管腐蚀速率预测

为提升海洋环境下深水区立管腐蚀速率的预测精度,建立基于改进秃鹰搜索算法(Improved Bald Eagle Search, IBES)的EDGM(1, 1,ρ)腐蚀速率预测模型。通过Sine混沌映射初始化种群、莱维飞行策略、折射反向学习策略和柯西高斯变异策略提高秃鹰搜索算法的寻优能力和收敛速度;利用IBES算法优化EDGM(1, 1,ρ)中的参数ρ,建立IBES-EDGM(1, 1,ρ)模型以提高立管腐蚀速率的预测精度。以南海某海洋深水区立管数据为基础进行腐蚀速率预测,分析对比3种模型的预测结果。结果表明,优化后的模型与原模型相比误差更小,且预测精度得到了提高,能够更准确地预测深海立管的腐蚀速率,为后续管道系统的维修和更换提供理论参考。

基于改进的SSA-BP神经网络的矿井突水水源识别模型研究

机器学习与寻优算法的结合在矿井突水水源识别上得到广泛应用,但突水水样数据具有随机性且寻优算法易陷入局部最优,提高模型泛化能力和跳出局部最优需进一步研究。针对上述问题,提出了一种改进的麻雀搜索算法(SSA)优化BP神经网络模型,用于对矿井突水水源进行定量辨识。以鲁能煤电股份有限公司阳城煤矿为研究对象,通过常规离子浓度分析、Piper三线图对该煤矿水样的水化学特征进行分析,初步判断矿井水来源于奥灰含水层和三灰含水层,并确定Na^(+)+K^(+)浓度、Ca^(2+)浓度、Mg^(2+)浓度、HCO_(3)^(-)浓度、SO_(4)^(2-)浓度、Cl^(-)浓度、矿化度、总硬度、pH值作为突水水源识别指标;建立基于改进SSA-BP神经网络的矿井突水水源识别模型:首先进行SSA参数设置,引入Sine混沌映射使麻雀种群均匀分布,然后通过计算适应度值进行麻雀种群的更新,引入随机游走策略扰动当前最优个体,如果满足终止条件,则获得最优BP神经网络权重和阈值,最后基于构建的BP神经网络,输出识别结果。研究结果表明:①改进的SSA-BP模型在训练集上的识别准确率达95.6%,在测试集上的识别准确率达100%。②改进的SSA-BP神经网络模型与BP神经网络模型、SSA-BP神经网络模型对比结果:BP神经网络模型误判率为5/18,SSA-BP神经网络模型的误判率为2/18,改进的SSA-BP神经网络模型误判率为0,迭代10次后趋于稳定,且与设定的目标误差相差最小,初始适应度值最优,识别结果可信度高。③将阳城煤矿5组矿井水水样数据作为输入层数据输入到训练好的模型中,矿井水水样的主要来源为奥灰含水层、三灰含水层和山西组含水层,模型识别结果与水化学特征分析的结论相互印证,实现了精准区分。

基于改进麻雀搜索算法的水质模型多参数反演

水质模型参数取值对模型的模拟精度影响很大,为提高BOD-DO水质模型参数反演精度,首先在DobbinsCamp BOD-DO水质模型的基础上,以BOD和DO浓度计算值与实测值之差的加权平方和最小为目标函数,构建了Dobbins-Camp BOD-DO水质多参数反演模型;然后针对麻雀搜索算法(Sparrow Search Algorithm,SSA)求解精度低、稳定性不足和易陷入局部最优等问题,引入Sine混沌映射和对立学习、转移概率以及差分变异3个策略,分别从提高初始种群多样性、扩大搜索空间以及增强种群跳出局部最优的能力三方面对SSA算法进行改进,提出了一种多策略改进的麻雀搜索算法(Multi-strategy Improved Sparrow Search Algorithm,MISSA),并将其应用于Dobbins-Camp BOD-DO水质多参数反演模型的求解;最后通过数值实验将得到的反演结果与SSA算法、模拟退火算法、粒子群算法、遗传算法四种优化算法进行对比,并探讨了参数初值选取和观测噪声水平对反演结果的影响。结果表明:MISSA算法的计算性能明显优于对照组中的4种算法,且能显著降低初值选取对BOD-DO水质模型参数反演结果的影响,当观测数据的噪声水平不超过5%时,MISSA算法可有效提高反演结果的稳定性。该结果验证了MISSA算法在反演Dobbins-Camp BOD-DO水质模型参数的有效性,为水质模型参数求解提供有益参考。

基于CEEMDAN-VMD-TCN-lightGBM模型的水质预测研究

针对目前水质预测模型中因为数据本身的复杂性、在信号处理过程中存在的噪声干扰以及分解深度不够导致单一分解难以全面捕捉信号非线性特征的问题,提出了一种基于二次分解的水质预测模型。该模型采用完全自适应噪声集合经验模态分解(CEEMDAN)对原始数据进行分解,再利用变分模态分解(VMD)对熵值最高的模态分量进行二次分解,最终将处理后的时间序列输入到TCN-lightGBM多特征预测模型中。同时,采用麻雀算法(SSA)对预测模型进行优化。以山东省玉符河水质为例,本模型的均方根误差(RMSE)是0.1053,平均绝对误差(MAE)是0.0815,决定系数(R2)是0.9471,与GRU、LSTM、LightGBM、TCN等当下较为流行的模型的预测指标进行比较。结果显示,在R2上本模型提升了53.04%、70.41%、66.07%、65.20%等,在RMSE上减少了62.76%、65.50%、64.93%、64.80%等,在MAE上降低了62.76%、66.24%、63.80%、65.24%等。由此可知,基于CEEMDAN-VMD-TCN-lightGBM的模型具有更好的预测性能、泛化能力和捕捉信号非线性特征的能力。

基于信创环境的水利智搜优化设计与研究

为提高水利智搜数据采集效率、个性化推荐程度、协调运维能力、信创环境适配程度,向社会公众提供便捷高效的水利信息检索服务,结合核心技术自主可控的必然要求,研究运用数据采集、搜索推荐模型等智能化处理技术,进一步优化搜索算法,加强数据信息采集。同时,通过优化水利智搜平台设计,基于信创环境适配优化,采用集成式、场景式和交互式一体化数据呈现方式,提升支撑、统计分析、搜索和聚合能力,实现精准化推荐和协同化运维。基于信创环境的水利智搜优化设计与研究,可为水利行业智能化信息检索服务提供经验借鉴。

引江济淮工程(河南段)多目标水量优化调度

引江济淮工程(河南段)涉及河道、闸泵、管道和调蓄水库,约束条件复杂,常规的优化调度算法难以搜索可行解,求解效率低。选用受水区缺水率平均值最小、泵站总抽水量最小和受水区缺水率标准差最小作为目标函数,从供水保障、供水成本和公平性角度构建多目标水量优化调度模型。基于可行搜索思路,结合逆序演算和顺序演算过程对约束条件进行处理,引入决策系数,通过映射关系使搜索空间保持在可行域中,结合多目标非支配排序遗传算法(non-dominated sorting genetic algorithms,NSGA-II)进行求解,得到Pareto最优解集,并采用熵权法进行方案优选。结果表明,基于可行搜索的NSGA-II算法能够有效求解复杂调度系统的多目标优化问题,综合考虑多个目标的最优方案相对单目标方案更加合理,结果可为引江济淮工程(河南段)运行管理提供决策支撑。

基于CSA-PLS算法的养殖水体水质快速高光谱预测反演模型研究

养殖水体水质的优劣直接影响养殖对象的成长,准确、快速、全面地掌控养殖水环境的水质参数变化情况具有重要意义。传统的水质指标监测方法都通过人工采样的方式,不仅耗费时间长,且只能体现局部水体情况。针对这些问题,提出了一种乌鸦搜索算法(CSA)结合偏最小二乘回归(PLSR)的高光谱特征波段筛选方法,快速构建回归模型,实现光谱数据的精准预测反演。以连片的养殖小区为研究对象,采集养殖水体样本并拍摄同时期的高光谱影像数据。首先对提取的采样点光谱数据利用多种数据变换方法分别预处理;其次利用这些数据,对水质指标总氮(TN)、氨氮(NH_(4)^(+)-N)、总磷(TP)和化学需氧量(COD)分别构建全波段的SVR和AdaBoost回归模型,同时与提出的CSA-PLS自动筛选波段方法和传统的连续投影算法(SPA)筛选波段后构建的模型进行比较分析;最后根据决定系数(R^(2))和均方根误差(REMS)选出适合各水质指标的最优模型。从实验结果可以看出,所提波段筛选方法的AdaBoost模型预测结果优于SVR和传统SPA方法提取特征波段后构建的模型,与全波段最优模型相比,在评价指标R^(2)和RMSE上TN提升了18.32%和10.73%;NH_(4)^(+)-N提升了17.42%和11.19%;COD提升了2.15%和2.54%。结果表明,基于CSA-PLS算法的光谱波段自动筛选方法结合AdaBoost构建的预测反演模型是有效、可行的,具有较高的精准度,为实现养殖水环境实时准确的预警调控提供了一种新的数据预测模型。