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Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data

文献类型：期刊论文

作者Iqbal, Sayed Muhammad3,4; Li, Jianmin2; Ma, Junxiu2; Hu, Dawei3,4; Tian, Shuang3,4; Zhou, Hui3,4; Shang, Litao1
刊名EARTH SCIENCE INFORMATICS
出版日期2025
卷号18期号:1页码:28
关键词Geomechanical Parameters Well Logging Data Levenberg-Marquardt Backpropagation (LM-BP) Scaled Conjugate Gradient (SCG) Mahu Sag
ISSN号1865-0473
DOI10.1007/s12145-024-01592-0
英文摘要Geomechanical parameters (GMPs), e.g., Poisson ratio, Young's modulus, and compressive strength of reservoir rock, are essential for design optimization, reducing operational risks, and wellbore stability assessment in geomechanical studies. Conventionally, these parameters are estimated by time-consuming laboratory experiments, in-situ tests, and empirical equations. However, these methods ignore the influence of scale and exhibit a nonlinear relationship, resulting in uncertain parameter estimations. To evaluate both the potentiality of machine learning (ML) methods and the contribution of various input variables for accurately estimating GMPs in complex geological datasets has not been broadly studied. Therefore, in this research, a two-layer feedforward artificial neural network (ANN) with well-established optimization algorithms, such as Levenberg-Marquardt (LM-BP) and Scaled Conjugate Gradient (SCG), were employed to calculate GMP from complicated data. To develop ML models, 15,705 datapoints of five wells from the complex Mahu field were utilized, and feature selection was used to determine significant input variables that aid in prediction performance, e.g., NPHI, DT, RT, and RHOB, respectively. Moreover, models were validated using 8,592 datapoints from three other wells. Evaluation of results showed that the LM-BP achieved RMSE values of 0.0011-0.00099 and R2 of 0.995-0.997, indicating superior prediction accuracy compared to SCG. It depicts parallel prediction precision when utilized with unseen datapoints. In addition, efficient and reliable training of ANN with LM-BP and SCG approaches better estimates GMP than empirical methods. Ultimately, this research advances geomechanical modeling and decision-making processes within the oil and gas industry.
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Geology
语种英语
WOS记录号WOS:001390673100001
出版者SPRINGER HEIDELBERG
源URL[http://119.78.100.198/handle/2S6PX9GI/37722]  
专题中科院武汉岩土力学所
通讯作者Hu, Dawei
作者单位1.CNPC Engn Technol R&D Co Ltd, Beijing 102206, Peoples R China
2.PetroChina Xinjiang Oilfield Co, Oil Prod Technol Res Inst, Karamay 834000, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
推荐引用方式
GB/T 7714		 Iqbal, Sayed Muhammad,Li, Jianmin,Ma, Junxiu,et al. Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data[J]. EARTH SCIENCE INFORMATICS,2025,18(1):28.
APA Iqbal, Sayed Muhammad.,Li, Jianmin.,Ma, Junxiu.,Hu, Dawei.,Tian, Shuang.,...&Shang, Litao.(2025).Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data.EARTH SCIENCE INFORMATICS,18(1),28.
MLA Iqbal, Sayed Muhammad,et al."Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data".EARTH SCIENCE INFORMATICS 18.1(2025):28.

入库方式： OAI收割

来源：武汉岩土力学研究所

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