Investigation of gas and water coning behavior for the enhancement of oil production

This study presents the extensive simulation to control the concurrent behavior of gas and water coning in oil reservoir with existence of a bottom aquifer. From simulation results, coning phenomena were observed even with the critical oil rate obtained analytically. It is because the critical rate is calculated using a steady state expression. In order to examine the coning behavior, firstly, we have run for various oil layer thicknesses. The result in case of thin layer shows early breakthrough of gas and water cones and the increase in water-oil ratio from the beginning of production. Meanwhile, for the thick case of 200 ft, there is no water breakthrough observed even though water cone has been already formed because it is stable. Since gas and water cones move mainly in a vertical direction, cone development is affected by a vertical permeability. As a result of runs for vertical permeabilities, the breakthrough time is getting delayed as the vertical permeability is smaller. In the case of a high vertical permeability, the shape of the water cone is developed in a concave form at the beginning. After two years of production, however, this cone shape becomes almost flat since the water-oil contact is elevated uniformly throughout the whole reservoir. In the analysis of coning behavior for different aquifer sizes, it is found that the aquifer size does not affect both cone shape and watercut. But with a strong bottom aquifer the behavior of gas coning is greatly decreased since the pressure is maintained by the active aquifer. The extent of well penetration into the oil layer has a considerable effect on coning phenomena. As the completion interval is decreased, the breakthrough time is delayed. However, a large pressure drop occurs in the shortest interval so that it worsens the well productivity. The most practical method to control coning is the oil production rate. Production of gas and water can be minimized by keeping oil rates as low as possible. However, a low rate is directly linked to well’s economics, and therefore, the optimizing process for the production rate is essential.     

Adaptive control design for a nonlinear parabolic PDE: Application to water coning

Water coning is usually responsible for the production of undesirable water from oil wells. This phenomenon may cause a decrease in oil production rate, increase in water cut production, and costs, which subsequently leads to early shutdown of the well. Although the boundary control of the production rate was suggested for managing the problem, due to the uncertainty associated with the physical nature of petroleum reservoirs, it failed to be implemented in practice. To overcome this issue, the paper employs the adaptive control approach for the distributed parameter system, which is modelled using a nonlinear partial differential equation (PDE). For this purpose, an adaptive control law and an update law for estimating the uncertain parameter are developed using the direct Lyapunov method. Next, the global stability of the closed‐loop system with the abovementioned laws is proven. Finally, the effectiveness and performance of the proposed idea is demonstrated by numerical simulations. The results show that the thickness of an oil column tends to zero as time tends to infinity for the whole spatial domain. In other words, as time elapses, the whole oil column will be depleted before the cone breakthrough. The numerical simulation demonstrates that though water cone breakthrough is inevitable in the conventional way of production, the adaptive control approach successfully controls the cone growth up, even with no knowledge of reservoir permeability. The results of this study can be applied to any type of reservoir subjected to water coning.

     

基于神经网络-遗传算法优化生物柴油制备工艺

根据生物柴油制备的实验数据,用人工神经网络(ANN)的反向传播(BP)算法建立了生物柴油转化率神经网络预测模型,提出了适宜的人工神经网络拓扑结构,讨论了BP算法中学习速率、动量系数及过拟合现象对网络的影响。实验数据检验表明,ANN方法能准确地关联生物柴油制备工艺条件与转化率的关系,转化率预测平均相对误差为1.917%,复相关系数R为0.9996;该神经网络预测模型用遗传算法优化,得到了最佳生物柴油制备条件。     

低渗透油田注水井深部调剖技术完善与推广

陇东油区油层属低产、低渗、低压三低油田,区块较多。各区块平面上、纵向上渗透率分布差异大,导致注入水或边底水沿高渗带、大孔道、裂缝指进或锥进,使油井过早见水,并很快水淹,降低了油田采收率。为了遏制油井含水上升过快的势头,提高水驱动用程度和采收率,研究适应陇东油区的堵水调剖技术,满足封堵大孔道、驱替小孔道残余油、剥离岩石孔隙中的油膜的目的,从而提高中高含水期的水驱效率,达到提高采收率的目的。本次研究综合运用示踪剂分析、脉冲试井、PI决策、水驱前缘测试4种方法,开展对储层的见水类型、见水方向、窜流程度研究与分析;对于三叠系裂缝油藏堵剂进行了优选与评价并且提出了堵水调剖方案优化设计方法。     

A developed smart technique to predict minimum miscible pressure—eor implications

Miscible gas injection (MGI) processes such as miscible CO₂ flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas–oil minimum miscibility pressure (MMP), which is normally determined through expensive and time‐consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas–oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas–oil MMP on the basis of a feed‐forward artificial neural network (FF‐ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO–ANN has the greatest impact on MMP value. The developed PSO–ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas–oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil–gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO–ANN model failed in providing a good estimate of the oil–gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran. © 2013 Canadian Society for Chemical Engineering     

底水油藏水平井中心管控水完井优化研究

对于底水油藏,采取常规水平井开发时,由于地层非均质性和流动摩阻的影响,底水锥进问题比较突出,含水率上升过快,对开发效果产生较大影响。采用中心管完井技术可以延缓底水锥进,平衡跟端生产压差,改变跟端液体流向,增加无水采油量,延长生产井寿命。以A油田某井为例,根据中心管完井压降耦合计算模型以及底水稳定机理推导出中心管见水时间计算公式。在给定套管内径尺寸和中心管外径尺寸的条件下分析了不同长度水平段的中心管见水时间的长短以及在给定中心管长度为300 m的条件下分析了不同管径尺寸的中心管见水时间的长短。     

块状底水裂缝型油藏含水上升规律及稳油控水对策

正确地认识和预测油田含水上升规律,是实施油田稳油控水,提高油田整体开发效果的一项基础性工作。X油田为一生物礁地层圈闭的块状底水裂缝型油田,油藏边地水能量充足,油水粘度比大。开采特征主要表现为见水早,含水上升速度快。本文根据油田PVT和动态数据对含水上升规律进行了深入研究,研究表明,整体压锥能显著提高油井产量、降低含水,有效改善油田开发效果,对指导本油田制定稳油控水措施及同类油田开发有重要意义。     

Gas Analysis by In Situ Combustion in Heavy‐Oil Recovery Process: Experimental and Modeling Studies

Enormous efforts have been made to facilitate produced‐gas analyses by in situ combustion implication in heavy‐oil recovery processes. Robust intelligence‐based approaches such as artificial neural network (ANN) and hybrid methods were accomplished to monitor CO₂/O₂/CO. Implemented optimization approaches like particle swarm optimization (PSO) and hybrid approach focused on pinpointing accurate interconnection weights through the proposed ANN model. Solutions acquired from the developed approaches were compared with the pertinent experimental in situ combustion data samples. Implication of hybrid genetic algorithm and PSO in gas analysis estimation can lead to more reliable in situ combustion quality predictions, simulation design, and further plans of heavy‐oil recovery methods.     

Modeling tensile modulus of (polyamide 6)/nanoclay composites: Response surface method vs. taguchi‐optimized artificial neural network

Tensile modulus is an important mechanical property of polymer/nanoclay nanocomposites. In this study, response surface method (RSM) and Taguchi‐optimized artificial neural network (Taguchi‐optimized ANN) were used to model tensile modulus as a function of nanoclay content, melt temperature, screw speed, and feeding rate for (polyamide 6)/nanoclay nanocomposites prepared in a twin‐screw extruder. The comparison between Taguchi‐optimized‐ANN‐ and RSM‐generated plots showed that predictions made by both models were in agreement in general. Coefficient of determination, R², showed that the RSM model can explain the variation with the accuracy of 0.768, indicating there was no strong correlation. However, from ANOVA, the p value for the RSM model was less than 0.05, signifying that the obtained model could be considered statistically significant. In addition, further assessment in terms of data fitting and prediction capabilities demonstrated the superiority of a properly trained Taguchi‐optimized ANN model in characterizing the nonlinear behavior of a response‐factors relationship. The Taguchi‐optimized ANN model R² for training data and testing data were 0.965 and 0.902, respectively. Also, the Taguchi‐optimized ANN model was developed by using 20% less data in comparison to the RSM model. J. VINYL ADDIT. TECHNOL., 22:29–36, 2016. © 2014 Society of Plastics Engineers     

气井凝胶堵水技术研究

气井产水是很普遍的生产现象,具有很大的危害。通过堵水处理能有效降低产水量,同时还能提高产气量,延长开发期,国外一些油田通过对气井堵水技术的研究,发展形成了一系列凝胶／气体注入技术应用于裂缝性气藏。进一步的成果是提高凝胶在基岩中的堵水效果。     

基于人工神经网络的咖啡因溶解度的建模

利用激光法测定的0-50℃温度范围内咖啡因在水和乙醇中的溶解度数据，经过比较选择了较好的人工神经网络模型—2-2-1向后传播(BP)人工神经网络模型，利用训练后的2-2-1BP人工神经网络模型对非线性的咖啡因溶解度数据进行了再现回归、内插和外推，在水中溶解度数据的回归、内插的误差均在0．07％以内，外推的误差在0．2％以内；在乙醇中的溶解度数据的回归误差在0，07％以内，内插和外推误差均在4％以内，效果十分令人满意。     

微地震裂缝监测技术在油水井压裂和注水评价中的应用

微地震裂缝监测技术使用现代声发射技术中平面任意三角形阵列的源定位方法,结合计算机数据处理技术和相关的油田地质参数,可在屏幕上实时显示数千米深岩层人工裂缝的变化形态和方位状况,开创性地应用高新技术手段指导油田开采,达到了提高工作效率、增加原油产量的目的。文章以微地震裂缝监测技术对火烧山油田压裂井监测及彩南油田注水井组监测结果,评价了注水井组的水窜方向和压裂井裂缝的形态、方向及长度,对油田压裂效果评价和油田后期注采井网调整起到了重要的指导作用。     

BP神经网络计算乙醇-环己烷-水体系汽-液平衡

基于带动量因子的 BP神经网络 ,以实验测定的乙醇 (1) -环己烷 (2 ) -水 (3)体系在 35℃、5 0℃、6 5℃的汽液平衡数据为训练和预测样本进行了计算 ,选择温度、X1 和 X2 3个参数作为输入 ,Y1 、Y2 和 Y3作为输出 ,隐层单元数为 9,学习速率为 0 .5 ,动量因子为 0 .12 8。对 Y1 ,Y2 ,Y3,神经网络计算的训练平均误差分别为 :0 .0 0 71,0 .0 101,0 .0 0 6 0 ,预测平均误差分别为 :0 .0 0 6 5 ,0 .0 12 4 ,0 .0 0 6 0 ,小于 NRTL 模型计算的相应误差。为相平衡计算提供了新的有效的工具。     

用人工神经网络模型模拟催化精馏塔

尝试用人工神经网络模型模拟醋酸甲酯水解催化精馏塔的操作过程,并寻求最佳工艺条件。以进料水酯比、回流进料比和醋酸甲酯的体积流率与催化剂体积比作为输入层的三个节点,醋酸甲酯的转化率和塔釜中的酸水比为输出层的两个节点,采用ＢＰ算法结合模拟退火算法,利用小试实验数据训练网络并检验训练结果;内插或外推一系列假想的工艺条件,让网络预测操作结果,最后确定最佳操作目标,通过网络模型寻找一组最佳工艺条件。结果表明：以充足可靠的实验数据为基础,神经网络模型能精确预测实验结果;利用人工神经网络模型对工艺条件进行优化,可取得令人满意的结果。     

基于神经网络-遗传算法优化制氢工艺水碳比

根据某炼油厂制氢车间的生产数据,用人工神经网络(ANN)的反向传播(BP)算法建立了制氢装置转化生产中的水碳比神经网络预测模型,生产数据的检验表明,ANN方法能准确地关联和预报制氢装置转化生产中的水碳比,水碳比预测平均相对误差为2．83％;该神经网络预测模型用遗传算法优化并得到了最佳制氢工艺操作条件。     

Performance evaluation of an ANN–GA aided experimental modeling and optimization procedure for enhanced synthesis of marine biosurfactant in a stirred tank reactor

BACKGROUND: An improved resilient back‐propagation neural network modeling coupled with genetic algorithm aided optimization technique was employed for optimizing the process variables to maximize lipopeptide biosurfactant production by marine Bacillus circulans. RESULTS: An artificial neural network (ANN) was used to develop a non‐linear model based on a 2⁴ full factorial central composite design involving four independent parameters, agitation, aeration, temperature and pH with biosurfactant concentration as the process output. The polynomial model was optimized to maximize lipopeptide biosurfactants concentration using a genetic algorithm (GA). The ranges and levels of these critical process parameters were determined through single‐factor‐at‐a‐time experimental strategy. Improved ANN‐GA modeling and optimization were performed using MATLAB v.7.6 and the experimental design was obtained using Design Expert v.7.0. The ANN model was developed using the advanced neural network architecture called resilient back‐propagation algorithm. CONCLUSION: Process optimization for maximum production of marine microbial surfactant involving ANN‐GA aided experimental modeling and optimization was successfully carried out as the predicted optimal conditions were well validated by performing actual fermentation experiments. Approximately 52% enhancement in biosurfactant concentration was achieved using the above‐mentioned optimization strategy. © 2012 Society of Chemical Industry     

合水地区致密油油藏注水井转采实施效果分析

致密油是继页岩气之后全球非常规油气勘探开发的又一新热点,被中国石油界广泛关注。研究表明,鄂尔多斯盆地延长组致密油成藏条件良好,主力层系为长6、长7油层组,盆地中部及南部深湖相沉积发育区是最有利的致密油勘探区。合水地区致密油油藏发育,储层物性差,具有显著的低渗、低压、低产特征,近年来通过水平井大规模体积压裂取得了较高的初产,但油井呈现出初产高、递减大、注水不见效、裂缝性见水的突出问题。对合水地区开展了致密油油藏注水井转采措施效果分析和探究,认为有必要对注采响应关系明显、裂缝性含水上升突出、注水长期不见效的井组内注水井实施转采措施,以提高油层动用程度。     

边水油藏油井提液技术研究

边水开发油藏在油井见水后,含水率上升加快,产油速度迅速下降,油井提液措施是保持稳产,提高最终采收率的有效开发模式。针对这类油藏,开展了提液技术研究,在油井提液六项基本原则的指导下,根据数值模拟结果优选提液部位、提液时机以及合理的提液幅度。该项技术对边水油藏中后期开发有着重要的指导意义。     

水驱气藏合理生产压差及优化配产

水驱气藏在己投入开发的气田中占很大的比重,对水驱气藏进行深入研究具有重要的理论意义和应用价值。本文对水驱气藏合理产量的研究是从单井研究出发,首先介绍了常规气井配产方法,并在掌握气藏地下、地面有关资料的基础上,建立了考虑气井积液,气液对管壁的冲蚀作用,底水锥进,地层气流的速敏效应和计划产量众多因素的综合配产模型,然后,根据气井的动、静态资料确定了解气井合理的生产压差。最后,利用实例气井资料进行计算,并验证该模型,认为该模型在这类气井配产中是适用的。能为现场配产提供一定的指导依据。     

Asphaltene precipitation and deposition in oil reservoirs – Technical aspects,experimental and hybrid neural network predictive tools

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO₂ injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.