
1. 中国石油大学(北京)石油工程学院 北京 102249
2. 中国石油大学(北京)碳中和示范性能源学院 北京 102249
3. 中国地质大学(北京)能源学院 北京 100083
Published Online:11 October 2025,
Published:2025
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姚学喆, 宋先知, 许争鸣, 周蒙蒙, 段世明, 崔悦琪, 王浩博. 基于差分进化算法的超深井静态地层温度随钻反演方法[J]. 石油学报, 2025, 46(9): 1805-1816.
Yao Xuezhe, Song Xianzhi, Xu Zhengming, et al. An inversion-while-drilling method for the static formation temperature of ultra-deep wells based on differential evolution algorithm[J]. Acta petrolei sinica, 2025, 46(9): 1805-1816.
姚学喆, 宋先知, 许争鸣, 周蒙蒙, 段世明, 崔悦琪, 王浩博. 基于差分进化算法的超深井静态地层温度随钻反演方法[J]. 石油学报, 2025, 46(9): 1805-1816. DOI: 10.7623/syxb202509012.
Yao Xuezhe, Song Xianzhi, Xu Zhengming, et al. An inversion-while-drilling method for the static formation temperature of ultra-deep wells based on differential evolution algorithm[J]. Acta petrolei sinica, 2025, 46(9): 1805-1816. DOI: 10.7623/syxb202509012.
静态地层温度是揭示油气运移机理、估算油气储量和计算井筒温度分布的重要基础数据。超深井储层温度高
由于井下仪器耐温能力有限和钻井时循环与停泵交替进行
导致直接测井法难以准确获取静态地层温度;而经验公式法由于需要在固定井深处的不同停泵时间下测量多个温度数据
导致其无法广泛应用。提出了一种基于差分进化算法的超深井静态地层温度随钻反演方法。考虑岩石热物性、钻井液流型和流道结构的影响
构建了地层—井筒瞬态传热模型
采用有限差分算法进行离散求解
最后结合实测井底循环温度数据
利用差分进化算法对超深井的地温梯度和静态地层温度进行反演求解。采用真实的超深井数据
对所建立的地层—井筒瞬态传热模型和静态地层温度反演模型进行验证。研究结果表明
地层—井筒瞬态传热模型预测的井底循环温度的平均相对误差为1.00%
静态地层温度反演模型的平均相对误差为1.44%。
Static formation temperature is a fundamental parameter for understanding hydrocarbon migration mechanisms
estimating oil and gas reserves
and modeling wellbore temperature distribution. In ultra-deep wells
the reservoir temperature is extremely high. Due to the limited temperature resistance of downhole instruments and the alternating circulation and shut-in during drilling
it is difficult to accurately obtain static formation temperature through direct logging. Empirical formula methods are unsuitable for widespread application
as they require multiple temperature measurements at a fixed depth under varying shut-in times. This study proposes a method for real-time inversion of static formation temperature in ultra-deep wells while drilling
which is based on the differential evolution algorithm. Considering the effects of rock thermal properties
drilling fluid flow patterns
and wellbore structure
a transient heat transfer model of the formation-wellbore system was established. The model was discretized for solution using a finite difference method. Based on the measured bottom-hole circulating temperature data
the differential evolution algorithm was then employed to invert for the geothermal gradient and static formation temperature in ultra-deep wells. Real data from ultra-deep wells were used to validate the proposed formation-wellbore transient heat transfer model and the static formation temperature inversion model. The results show that the bottom-hole circulating temperature predicted by the transient heat transfer model has a mean relative error of 1.00 %
while the static formation temperature invertion model yields a mean relative error of 1.44 % .
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