Liang Jiwen, Luo Ming, Li Wentuo, Sun Bo, Yan Chuanliang, Han Zhongying, Cheng Yuanfang
Hainan Branch of CNOOC (China) Co., Ltd, Haikou, 570100, Hainan, China.
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong, China.
Sci Rep. 2025 Jul 2;15(1):23357. doi: 10.1038/s41598-025-07048-9.
The difficulty of accurately predicting abnormally high-pressure formation pressure is one of the critical challenges in the field of petroleum engineering. Due to the low accuracy of formation pressure prediction and the narrow drilling safety density window, accidents such as leakage and blowout occur frequently. To address this issue, improving the accuracy of pore pressure predictions is essential. The well logging and mud logging data were combined to analyze the correlation between various parameters. Analysis using the Spearman correlation coefficient revealed that pore pressure exhibits varying correlation relationships with different parameters. Pore pressure is closely related to factors such as depth, weight of hanging, and mud weight. Pore pressure has a medium to high correlation with the rate of penetration, weight on bit, torque, slurry pump pressure, acoustic time difference, density, and volume of clay. Pore pressure has a medium to low correlation with the rotation per minute. Based on machine learning algorithms and a large amount of known data, a machine learning formation pressure model with integrated well logging and mud logging data (IWM) was established. The prediction results of traditional models and IWM models were compared using neighboring wells as the prediction targets. The results indicate that the backpropagation neural network model based on a genetic algorithm and IWM (IWM-GABP) achieves the highest prediction accuracy, with an average prediction accuracy greater than 96%. When predicting formation pressure, it is advisable to use the back propagation neural network model based on IWM or the IWM-GABP model, rather than the radial basis function neural network model based on IWM. The IWM model significantly reduces the prediction error of formation pore pressure, achieving an average improvement of 8.32% enhancement in prediction accuracy compared to traditional data models. The research method effectively improves the accuracy of formation pressure prediction and provides support for efficient on-site development.
准确预测异常高压地层压力的难度是石油工程领域的关键挑战之一。由于地层压力预测精度低且钻井安全密度窗口窄，漏失和井喷等事故频繁发生。为解决这一问题，提高孔隙压力预测精度至关重要。将测井和录井数据相结合，分析各种参数之间的相关性。使用斯皮尔曼相关系数进行分析表明，孔隙压力与不同参数呈现出不同的相关关系。孔隙压力与深度、悬重和泥浆比重等因素密切相关。孔隙压力与机械钻速、钻压、扭矩、泥浆泵压力、声波时差、密度和粘土体积具有中到高的相关性。孔隙压力与每分钟转速具有中到低的相关性。基于机器学习算法和大量已知数据，建立了一个集成测井和录井数据的机器学习地层压力模型（IWM）。以邻井为预测目标，比较了传统模型和IWM模型的预测结果。结果表明，基于遗传算法和IWM的反向传播神经网络模型（IWM-GABP）预测精度最高，平均预测精度大于96%。预测地层压力时，建议使用基于IWM的反向传播神经网络模型或IWM-GABP模型，而不是基于IWM的径向基函数神经网络模型。IWM模型显著降低了地层孔隙压力的预测误差，与传统数据模型相比，预测精度平均提高了8.32%。该研究方法有效提高了地层压力预测精度，为高效现场开发提供了支持。
