Investigating the efficiency of gas re‐injection process of an oil field using combined integrated field simulation and intelligent proxy model application

Integrated asset modelling is a novel method to overcome the limitations associated with using individual models. This method integrates all the individual models of a field into a single model that relates all the sub‐models using proper boundary conditions. Reservoir, wells, surface, and economic models of an oil reservoir, under gas re‐injection, are integrated. The main goal of this study is to propose a novel approach in integrated asset modelling. An integrated model of a field is used to study how gas must be distributed among injection wells. Another aim of this study is to understand the effects of 4 input parameters on the Net Present Value (NPV) of the field. The input variables are: oil production rate, gas injection rate, and the distribution of gas between injection wells. A comprehensive model of a field was built. Using the experimental design results, a neuro‐fuzzy logic network was developed. The proxy model predicted the simulation outputs with a reasonable accuracy. The effects of input variables were studied. Oil production has an optimum value of 6050 STBD per well. The optimum fractions of injected gas for injection wells 1 and 2 are 0.4 and 0.6 of total injected gas, respectively. This means that 40 % of the total injection gas must be injected to well 1 to have the maximum NPV. The greater the gas injection rate, the higher NPV is.

     

Integrated gas lift system optimization

An intelligent genetic algorithm has been developed to simultaneously optimize all effective factors, namely, gas injection rate, injection depth and tubing diameter towards the maximum oil production rate with the water cut and injection pressure as the restrictions. The results were field tested in three wells in three different fields located in southern Iran. The field data verified the accuracy of the used model. There was a notable production increment compared to the master development plan of the fields.     

SOFT COMPUTING METHOD FOR PREDICTION OF CO2 CORROSION IN FLOW LINES BASED ON NEURAL NETWORK APPROACH

An important aspect of corrosion prediction for oil/gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data, and theoretical models to obtain realistic assessments of corrosion rates. This study presents a new model to predict corrosion rates by using artificial neural network (ANN) systems. The values of pH, velocity, temperature, and partial pressure of the CO₂ are input variables of the network and the rate of corrosion has been set as the network output. Among the 718 data sets, 503 of the data were implemented to find the best ANN structure, and 108 of the data that were not used in the development of the model were used to examine the reliability of this method. Statistical error analysis was used to evaluate the performance and the accuracy of the ANN system for predicting the rate of corrosion. It is shown that the predictions of this method are in acceptable agreement with experimental data, indicating the capability of the ANN for prediction of CO₂ corrosion rate in production flow lines.     

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     

靖边油田油水井调剖堵水效果分析

油气田开发过程中有一个普遍的问题即油井出水。油井出水会对油气井生产、油气集输与油气田开发产生的影响,尤其对注水开发油田影响深远。所以在油气田开发过程中了解油气井出水的情况,及时的找出出水层位,并且能够采用相对应堵水的办法是很重要的。堵水调剖技术能有效的优化高含水油田注水开发的效果,从而实现油气田的稳产。     

VideoLog可视化测井系统在落鱼检测中的应用

在进行井下作业的过程中,由于井下落鱼(井下落物)、岩石断裂等造成套管遇阻、错断变形等事故常有发生,直接影响油田油、气产量和注水效果及油气井的寿命,同时也造成经济损失和地层环境污染。介绍了一种通过高清摄像头获取井下情况,并采用测井电缆高速遥传技术传至地面实时显示的可视化测井系统,该系统能够检测鱼顶形状并获取目标检测段的高清视频图像,为落鱼打捞提供直观、可靠的重要资料。实际应用表明,可视化测井系统在油气井井下落鱼检测中具有广阔的应用前景。     

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。     

Effect of secondary gas injection on the particle entrainment rate in a gas fluidized bed

The effect of secondary gas injection on the particle entrainment rate was measured in a cold model fluidized bed (i.d. 0.1 m, height 2.25 m) and discussed. Sand particles below 0.425 mm in screen size were used as bed materials. The particle size (0.128–0.363 mm), the overall superficial gas velocity (0.78–2.76 m/s), the secondary gas fraction (0–0.5), and the static bed height (0.1–0.3 m) were considered as experimental variables. The particle entrainment rate decreased with an increase of the secondary gas fraction. The injection level of the secondary gas was shown to have an important influence on its effect. The effect of the secondary gas was appreciable for over-bed injection; the effect was reduced, however, when the injection level was placed in the splash zone or in the dense phase by means of increasing the bed height.     

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.     

本井气柱塞气举技术研究

吐哈油田自91年投产以来,随着油田的开发地层压力逐年下降而气油比呈急剧上升趋势,特别是温米油田出现了大批的高气油比油井,这给目前采用的有杆泵和电潜泵举升带来了一定的难度。吐哈油田气藏为凝析气藏,埋藏深,单井产能低,气井开发滑脱损失较大,井底积液较多,造成产气量的较大损失,目前采用的泡沫排水采气工艺效果不理想,发泡剂消耗大,泡排作业频繁,排水采气技术成本较高,亟需开发适应油田特点的新型排水采气工艺技术。而本井气柱塞气举能很好的适应高气油比和气井排水采气的特点。为此,我们开展了本井气柱塞气举技术研究,并进行了现场试验,取得了良好的效果,为油田部分低含水高气油比油井的开发和气井的排水采气提供了有效的举升技术支持。     

有效驱替理论在低渗透油藏中的应用分析

在低渗透油藏,注水井附近地层具有较高的注水压差,因而也具有较高的视渗透率。在生产井附近地层同样也具有较高的生产压差,和较高的视渗透率,可称为易流动半径。但是在低渗透储层中,易流动半径很小。而注采井问有很长的压力平缓段,压力梯度低,视渗透率也低,渗流能力很低,称为不易流动带,是影响低渗透油藏有效开发的主要因素。减小注采井距,在相同的压差下压力平缓段减小,压力梯度增大,视渗透率也有较大提高。在较高的压力梯度和较高视渗透率的双重作用下,渗流量会有较大的提高,水驱效果得到改善。为此,对低渗透油田适当减小注采井距,可以建立较大的驱替压力梯度和有效的驱油效果,能改善注水状况和采油状况,提高采油速度和最终采收率。     

Recovery of flared gas through crude oil stabilization by a multi-staged separation with intermediate feeds: A case study

Since the birth of the oil industry, flaring has been used upstream to depressurize eruptive wells and downstream to burn excess gases in refining and petrochemical plants and also in associated and natural gas treatment plants. Unfortunately, flaring produces a great number of harmful by-products such as dangerous particles, volatile organic compounds, polycyclic aromatic and many other compounds just as harmful. The separation of gas and oil phases remains the most important stage in the so-called surface production. Given the high gas oil ratio (GOR) of Algerian crude oil, the separation of this gas is carried out in three or four stages. However, the choice of the optimal number of stages of separation and intermediate values of pressure requires a rigorous computation wherein the use of a simulator is more than necessary to make possible the optimization of the separation process. The present work was performed on a new separation and compression unit in an area where all the associated gas production is being flared despite the new environmental laws. Our approach consists of first simulating the separation process with the most appropriate thermo dynamical model. The intermediate separation pressure values can be determined by empirical correlations such as the method of equal pressure ratio. In our computations we have opted for a graphical method, specifically the method of minimum compression energy, that requires rigorous calculations entailing therefore the use of the Hysis simulator. This treated gas may be valorized as a raw material for the petrochemical industry or compressed and re-injected into the reservoir in order to maintain the rate of oil production. It remains that one important way of valorizing this associated gas is to transform it into liquid through a process known as gas to liquid (GTL) technology.     

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...     

人工地震采油技术机理研究与分析

人工地震采油技术是利用一定的设备在油层附近产生频率很低的机械波,通过声波的特性在油层发生一系列的波动效应,达到多口井增产增注的一种物理采油方法。它是一种适应低渗透、高含水中后期油田开发的比较理想的物理法采油技术。作者对其作用机理进行了理论分析、归纳和总结,为该技术的进一步深入研究做铺垫。     

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.     

Hydrodynamics study of a fast liquid–liquid oxidation process with in situ gas production in microreactors

Hydrodynamics characteristics of a fast and highly exothermic liquid–liquid oxidation process with in situ gas production in microreactors were studied using a newly developed experimental method. In the adipic acid synthesis through the K/A oil (the mixture of cyclohexanol and cyclohexanone) oxidation with nitric acid, bubble generation modes were divided into four categories. The gas production became more intensive, unstable, even explosive with increasing the oil phase feed rate and the temperature. A novel automatic image processing method was developed to monitor the instantaneous velocity online by tracking the gas–liquid interface. The axial velocity at the same location was unstable due to the changing gas production rate. Furthermore, the actual residence time was obtained easily with being only 36% of the space–time minimally, beneficial for establishing accurate kinetics and mass transfer models with time participation. Finally, an empirical correlation was developed to predict the actual residence time under different conditions.     

Analytical study on the optimum design of producing well to increase oil production at severe cold regions

This paper presents a simulation study of fluid flow in the tubing on a reservoir system formed in a severe cold region. The thickness of the permafrost and specific gravity of the oil were applied by field survey. Then, flowing improvement techniques for oil production such as progressive cavity pump (PCP), insulated casing, electric trace heater and gas lifting were applied. For the reservoir located at 1,000 m depth in the Arctic region, the thicker the permafrost layer was, the more the mobility of oil in the tubing declined. By applying the flowing improvement techniques to this reservoir, the effect of the heater increased with the oil containing heavier components, and it was found that the production rate was improved as the heater installation interval became deeper. Despite the gas lifting method showing better productivity compared to other methods, there was an optimal injection rate at which the production rate became maximum. Moreover, it was shown that increasing the temperature of injection gas had little effect on enhancing the oil flow in tubing. Based on these results, flowing improvement techniques were applied to the oil wells in the Ada field. The productivity by PCP of Bashenkol_1X well, which contained comparatively light oil, increased 3.75 times more than natural state. Also, additional installation of insulated casing could yield better production. In the case of Bashenkol_3X in which 19.2° API of heavy oil was reserved, oil production was impossible without flowing improvement methods. This well was able to produce 158 BOPD of oil by installing PCP with insulated casing and additional installation of heater increased production rate to 267 BOPD. Meanwhile, although the gas lifting method can greatly improve productivity, the applicability and cost should be considered prior to its being applied.     

人工神经网络模拟超临界萃取米槁精油

为了更好地研究超临界CO2流体萃取米槁精油过程,文中对该过程萃取动力学进行了分析,建立了3层反向传导的人工神经网络模型。以MATLAB软件为平台,编制模型程序。以温度、压力、超临界CO2流体流率和原料平均粒径为输入,以收率为输出对网络进行训练。训练好的模型可用于萃取过程预测,与实验结果比较,预测平均误差小于5.0%。说明该模型可以对萃取过程进行模拟和预测。     

水力压裂裂缝导流能力的影响因素分析

水力压裂是油气井增产、注水井增注的一项重要的技术措施,其目的是提供一条连通地层与井筒的高导流能力通道,改变地层流体的渗流方式,提高生产指数。裂缝导流能力是指支撑裂缝所能提供液体流动的能力的大小,而影响裂缝导流能力大小的因素可归结为:储层条件、支撑剂物理性质、压裂工艺水平和压后生产管理这四个方面,并对这些因素进行了分析。     

Using Artificial Neural Network to Predict the Pressure Drop in a Rotating Packed Bed

Although rotating beds are good equipments for intensified separations and multiphase reactions, but the fundamentals of its hydrodynamics are still unknown. In the wide range of operating conditions, the pressure drop across an irrigated bed is significantly lower than dry bed. In this regard, an approach based on artificial intelligence, that is, artificial neural network (ANN) has been proposed for prediction of the pressure drop across the rotating packed beds (RPB). The experimental data sets used as input data (280 data points) were divided into training and testing subsets. The training data set has been used to develop the ANN model while the testing data set was used to validate the performance of the trained ANN model. The results of the predicted pressure drop values with the experimental values show a good agreement between the prediction and experimental results regarding to some statistical parameters, for example (AARD% = 4.70, MSE = 2.0 × 10^?5 and R² = 0.9994). The designed ANN model can estimate the pressure drop in the countercurrent flow rotating packed bed with unexpected phenomena for higher pressure drop in dry bed than in wet bed. Also, the designed ANN model has been able to predict the pressure drop in a wet bed with the good accuracy with experimental.