摘要
评价油井产能对生产开发具有重要指导意义,而针对于非均质性极强缝洞型碳酸盐岩储层,常规产能评价方法有着诸多的不适应性。表现在井底流压测试困难,地层压力衰减较快,求取的采油指数也仅反映测试时产能特征。为更有效的对油井产能动态特征进行分析评价,通过油压及日产量构建产能分析图版,分析油井产能递减率,根据油藏驱动能量特征,将油藏区分为水压驱动油藏和弹性驱动油藏。并利用油压与日产量回归得到直线投影到横坐标得到的产能面积,与单井自喷期实际的日产油能力进行统计回归,得到产能面积与实际日产油能力的相关经验公式,即产能评价预测经验公式。依据此公式只需要判别油藏驱动类型,利用油压及日产量数据计算产能面积,即可求得自喷期单井日产油能力。该方法简单实用,可有效的对本区块油井自喷期日产能力进行评价预测。无需任何测试费用,同时也为类似油田油井产能评价分析提供一种技术思路。
Evaluation of oil well productivity has important guiding significance for production and development of reservoirs. Conventional productivity method has a lot of inadaptabilities in evaluating fractured carbonate reservoirs with strong heterogeneity, including that the bottom hole flow pressure test is difficult, the formation pressure decreases rapidly, and the oil recovery index also only reflects the capacity in the test. In order to analyze and evaluate the dynamic characteristics of oil well productivity more effectively, the productivity analysis chart of oil pressure and output was constructed. Oil well productivity decline rate was analyzed. According to the energy characteristics of the reservoir, the reservoir was divided into water pressure driven reservoir and elastic driven reservoir. The regression of oil pressure and daily output was used to get the production area of the linear projection to the horizontal coordinate. Statistical regression of production area and oil production capacity of actual flowing period was carried out to obtain empirical formula of production area and actual production capacity, that was the empirical formula of productivity evaluation and prediction. According to this formula, through distinguishing the reservoir type, based on the production area calculated by oil pressure and output data, single well capacity in the flowing period was obtained. This method is simple and practical, can effectively evaluate the production capacity of flowing period.
出处
《当代化工》
CAS
2017年第7期1409-1411,共3页
Contemporary Chemical Industry
关键词
碳酸盐岩
缝洞型油藏
产能评价
产能系数
产能预测
Carbonate rock
Fractured-vuggy reservoir
Productivity evaluation
Deliverability coefficient
Productivity prediction