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水侵砂岩气藏型储气库注采气能力预测方法 被引量:14

A prediction method for gas injection and production capacity of underground gas storages rebuilt from water-invasion sandstone gas reservoirs
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摘要 注采气能力预测是水侵砂岩气藏型地下储气库多周期运行动态跟踪评价的一个重要环节,可为地下储气库优化配产配注及方案调整等提供科学依据。针对水侵砂岩气藏型地下储气库气水多相渗流的特点,从总垫气量和工作气量变化率的定义出发,基于盘库技术指标的计算方法,提出了地下储气库多周期注采气能力预测的数学模型。所建立的预测模型除考虑地下储气库扩容对注采气能力的影响外,还考虑了气水互驱过程中存在的注入气损耗,从而使预测数据的规律性和可靠性大大增强。应用实例表明:该模型预测精度高、适用性好,完全能够满足地下储气库矿场的应用需求。 Gas injection and production capacity prediction is an important link for dynamic evaluation of the multi-cycle operation performance on underground gas storages(UGS) rebuilt from water-invasion sandstone gas reservoirs. It can provide the scientific basis for UGS injection-production optimization and program adjustment. In this paper, the change rate of total cushion gas and working gas was defined. Then, based on the gas-water multiphase flow characteristics of UGS rebuilt from water-invasion sandstone gas reservoirs, a mathematical model for predicting its multi-cycle gas injection and production capacity was established by means of the inventory verification parameter calculation method. This prediction model takes into account not only the effect of UGS capacity expansion on gas injection and production capacity, but also the injected gas loss in the process of water-gas mutual flooding, so the prediction result is much more reliable and regular. It is shown from field application that this prediction model can satisfy UGS field application with high accuracy and good applicability.
作者 李春 王皆明 赵凯 王玲欣 LI Chun WANG Jieming ZHAO Kai WANG Lingxin(Langfang Branch of PetroChina Research Institute of Petroleum Exploration & Development CNPC Key Laboratory of Oil & Gas Underground Storage Engineering The Second Logging Branch, CNPC Bohai Drilling Engineering Company Limited)
出处 《油气储运》 CAS 北大核心 2017年第3期274-278,共5页 Oil & Gas Storage and Transportation
基金 中国石油天然气集团公司重大科技专项"板桥库群提高工作气量工程试验研究" 2015E-4007
关键词 水侵砂岩气藏 地下储气库 注采气能力 预测 数学模型 water-invasion sandstone gas reservoirs underground gas storages gas injection and production capacity prediction mathematical model
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