基于微机平台的注塑模型腔CAD/CAM一体化的实现

本文分析了注塑模型腔CAD／CAM实践中具体的曲线曲面造型处理方法，讨论了CAD／CAM实现中的各项关键技术，并成功地在微机系统上实现了注塑模型腔CAD／CAM的一体化。     

Case study: Comparison of enhanced heavy-oil recovery by CGI,WAG, and GAGD

Continuous gas injection (CGI) in the conventional horizontal flooding patterns leads to severe gravity segregation and poor reservoir contact (sweep) volumes. To improve the sweep efficiency, the Water-Alternating-Gas (WAG) process has been widely practiced in the industry. However, the WAG process has not measured up to expectations, as evidenced by the low recoveries observed in several field cases.

The Gas-Assisted Gravity Drainage (GAGD) process is designed and practiced based on gravity drainage idea and uses the advantage of density difference between injected gas and reservoir oil to overcome the drawbacks of the WAG process.

In this study, a comparison of enhanced heavy-oil recovery by three methods (CGI, WAG, and GAGD) is conducted by a commercial numerical simulator in a sector model of an Iranian offshore field. For this purpose, different production scenarios are designed. Moreover, the effect of several parameters such as injection fluid (CO₂, hydrocarbon gas), in situ oil viscosity (heavy or extremely heavy oil), rate and volume of injection, and also three different WAG ratios are studied.

The results show that the GAGD method is not suitable for this field as the thickness of pay zone is low for vertical sweep efficiency and causes early breakthrough of injected gas. Also, CGI leads to lower recoveries compared to the WAG process, due to its unfavorable mobility ratio. Injection of CO₂ instead of hydrocarbon gas results in higher sweep efficiency because of its viscosity reduction and swelling effects. In case of extremely heavy-oil recovery, with decreasing well spacing, the oil recovery factor increases as the drainage radius of wells reduces substantially due to high-viscosity oil.     

Hybrid Neural Network Performance Prediction Model for Gas Assisted Gravity Drainage Recovery Method Based on the Scaling Analysis

Hybrid system is a potential tool to deal with nonlinear regression problems. The authors present an efficient prediction model for gas assisted gravity drainage injection recovery process based on artificial neural network (ANN) and dimensionless groups. Ant colony optimization (ACO) is applied to determine the network parameters. Results show that ACO optimization algorithm can obtain the optimal parameters of the ANN model with very high predictive accuracy. The predicted recovery from the ACO-ANN model, in comparison with other proposed models in literature, were in good agreement with those measured from simulations, and were comparable to those estimated from the other proposed models.     

顶部注气油藏定量评价筛选方法

为筛选适宜采用顶部注气稳定重力驱提高采收率的高含水油藏,基于大量成功区块的统计样本,借鉴权重评价方法,综合运用灰色关联、试验设计、响应曲面法等数理统计理论,并引入无因次数群对因素进行组合,从而给出了顶部注气稳定重力驱筛选标准、适宜区域和敏感因素最优范围3个定量化判断依据,建立了考虑因素更全面、结果应用更直观、可信度更强的定量化高含水老油田顶部注气评价筛选方法。通过实际计算结果表明:顶部注气筛选标准为适宜度权重大于0.59;正韵律地层存在2个顶部注气适宜区域(第一适宜区域为变异系数小于0.75,无因次数群N_a小于4 802;第二适宜区域为变异系数小于0.75,无因次数群N_a大于28 215);润湿指数范围为0.5~0.6、变异系数小于0.75、地层倾角大于13°的正韵律厚层油藏更适合采用顶部注气开发。该方法在冀东油田高5断块的应用表明,该断块在高含水后期采用顶部注气提高采收率是可行的。     

利用CT技术研究重力稳定注气提高采收率机理

重力稳定注气(GAGD)已被证明是一种低成本且能大幅提高采收率的有效技术。笔者以80～200目石英砂混填制作的填砂模型为研究对象,利用CT扫描测量三相流体饱和度技术,开展了高含水后期重力稳定注气提高采收率实验研究。实验结果表明,水驱后采用顶部垂直注气方式能进一步提高采收率7.45%以上。通过CT扫描测量流体饱和度方法可以观察到,由于气液之间的重力差异,注入气驱扫区域内的液体持续向下运移,致使之前水驱形成的残余油“聚并”而形成“富集带”,随着注入气逐渐向底部推进。