摘要
水平地应力是井壁稳定分析和水力压裂改造的关键基础参数,但深部地层地质环境复杂且隐蔽,使得水平地应力的准确快速预测难度较大。考虑到传统测井解释和神经网络模型难以描述测井数据与地应力之间的空间相关性,提出采用一种基于双向长短期记忆神经网络(BiLSTM)的水平地应力预测方法;以四川盆地CL气田两口直井为例,将两口直井分别作为训练井和测试井,通过训练井建立测井参数与地应力之间的非线性映射关系,实现对测试井水平地应力的预测;结合测井参数相关性和实际地质含义,分析了不同测井参数组合模式下水平地应力的预测效果。研究结果表明:(1)对比测井解释和岩心差应变测试结果发现,垂向地应力测井解释误差为0.39%,最大水平地应力测井解释误差为0.18%~0.64%,最小水平地应力测井解释误差为0.29%,说明测井解释与实际地应力吻合较好;(2)工区地应力大小排序为垂向地应力>最大水平地应力>最小水平地应力,属于潜在正断层应力状态;(3)水平地应力与垂深、密度和自然伽马呈较强的正相关关系,与纵波时差、井径、补偿中子和横波时差呈负相关关系;(4)不同的测井参数组合对水平地应力的预测效果不同,其中最优的测井参数组合为垂深、井径、密度、补偿中子、自然伽马、纵波时差;(5)通过正交设计实验,确定了最优超参数取值方案,其预测得到的最大和最小水平地应力平均绝对百分比误差分别为0.48‰和0.50‰。结论认为,BiLSTM模型能够有效捕捉测井参数随深度的变化趋势和测井参数的前后关联信息,可以实现水平地应力的精准预测。
Horizontal in-situ stress is the key basic parameter of wellbore stability analysis and hydraulic fracturing, but the geological environment of deep formations is complicated and hidden, which makes it difficult to predict the horizontal in-situ stress accurately and quickly. Considering that the traditional logging interpretation and the neural network model cannot describe the spatial correlation between logging data and in-situ stress, a horizontal in-situ stress prediction method based on a Bidirectional Long Short-Term Memory neural network(Bi LSTM) was proposed. Taking two vertical wells in the CL gas field in the Sichuan Basin as an example, two vertical wells were taken as the training well and test well respectively, and the nonlinear mapping relationship between logging parameters and in-situ stress was established through the training well, so as to realize the prediction of horizontal in-situ stress of the test well. Combined with the correlation of logging parameters and the actual geological meaning, the prediction effect of horizontal in-situ stress under different combination modes of logging parameters was investigated. The results indicated that:(1) Comparing the logging interpretation and core differential strain testing results, it is found that the logging interpretation error of vertical stress is 0.39%, the logging interpretation error of maximum horizontal in-situ stress is 0.18%~0.64%, and the logging interpretation error of minimum horizontal in-situ stress is 0.29%, which indicated that the logging interpretation is in good agreement with the actual in-situ stress.(2) The order of in-situ stress in the working area is vertical stress > maximum horizontal in-situ stress > minimum horizontal in-situ stress, which belongs to potential normal fault stress state.(3) There is a strong positive correlation between horizontal in-situ stress and true vertical depth(TVD), density(DEN), and natural gamma ray(GR), and a negative correlation between horizontal in-situ stress and interval transit time of P-wave(DTC), borehole diameter(CAL), compensated neutron(CNL) and interval transit time of S-wave(DTS).(4) Different combination modes of logging parameters have different prediction effects on horizontal in-situ stress, the optimal combination of logging parameters is TVD, CAL, DEN, CNL, GR, and DTC.(5) Orthogonal experiments are designed to optimize hyper parameters, and the average absolute percentage errors of maximum and minimum horizontal in-situ stress are 0.48‰ and 0.50‰, respectively. It is concluded that the Bi LSTM model can effectively capture the variation trend of logging parameters with depth and the correlation information of logging parameters, and it can realize the accurate prediction of horizontal in-situ stress.
作者
马天寿
向国富
石榆帆
桂俊川
张东洋
MA Tianshou;XIANG Guofu;SHI Yufan;GUI Junchuan;ZHANG Dongyang(State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500,China;School of Engineering,Southwest Petroleum University,Nanchong 637001,China;Shale Gas Research Institute,PetroChina Southwest Oil&Gas Field Company,Chengdu 610051,China)
出处
《石油科学通报》
2022年第4期487-504,共18页
Petroleum Science Bulletin
基金
四川省杰出青年科技人才项目(2020JDJQ0055)
南充市市校科技战略合作项目(SXHZ033)联合资助。
作者简介
通讯作者:马天寿,matianshou@126.com。
