Results 251 to 260 of about 2,839,750 (318)

Real-Time 3D Anisotropy Analysis Enables Lithology Identification at Distance

SPWLA 63rd Annual Symposium Transactions, 2022
Ultra-deep ElectroMagnetic (EM) inversion is used to resolve multiple layers far from the wellbore. Since it primarily responds to resistivity variations, shale and water bearing sand layers of similar resistivity values can’t be resolved using a traditional resistivity inversion alone.
Elkhamry, Ayman, Taher, Ahmed, Bikchandaev, Eduard, Fouda, Mohamed   +3 more
openaire   +1 more source

ResGAT: A Residual Graph Attention Network for Lithology Identification

IEEE Geoscience and Remote Sensing Letters
Lithology identification is crucial for oil and gas exploration and reservoir evaluation, involving the analysis of physical and chemical characteristics of geological samples through well-logging data.
Fengda Zhao, Zihan Zhou, Haobing Zhai, Pengwei Zhang, Xianshan Li   +4 more
semanticscholar   +1 more source

Evaluation of active learning algorithms for formation lithology identification

Journal of Petroleum Science and Engineering, 2021
Abstract Lithology identification using well log data plays an important part in formation characterization and reservoir exploration. Recent years, the development of machine learning has provided new technologies for lithology identification research. Most of the existing studies utilized supervised learning algorithms, which needs a large quantity
Ting Xu, Ji Chang, Deyong Feng, Wenjun Lv, Yu Kang, Haining Liu, Jing Li, Zerui Li   +7 more
openaire   +1 more source

A Lithology Identification Approach Using Well Logs Data and Convolutional Long Short-Term Memory Networks

IEEE Geoscience and Remote Sensing Letters, 2023
Lithology identification plays a crucial role in formation characterization and reservoir exploration. When available core samples are limited, well logs data becomes important in lithology identification.
Jun Wang, Junxing Cao
semanticscholar   +1 more source

Identification of lithology in the Gulf of Mexico

The Leading Edge, 1998
In a small town outside of Houston, a local rancher was overheard saying, “Do you have any 3-D seismic across your place? You ought to get some, because it tells you exactly what’s down there and where to drill.” Yes, the transfer of technology has been accelerated by new electronic media such as the Internet, but is it possible that it bypassed the ...
Fred Hilterman, John W. C. Sherwood, Robert Schellhorn, Brad Bankhead, Brian DeVault   +4 more
openaire   +1 more source

MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area

Remote Sensing
Lithology classification stands as a pivotal research domain within geological Remote Sensing (RS). In recent years, extracting lithology information from multi-source RS data has become an inevitable trend.
Jiaxin Lu, Liangzhi Li, Junfeng Wang, Ling Han, Zhaode Xia, Hongjie He, Zongfan Bai   +6 more
semanticscholar   +1 more source

Lithology Identification Using Drill Bit Sounds from the Lab

International Geomechanics Symposium, 2023
Abstract Real time lithology information can tremendously impact drilling by providing input for geo-steering, casing shoe positioning, and subsurface hazards detection. Conventional logging while drilling (LWD) tools typically predict and provide lithology related information at a lag distance behind the drill bit.
W. Li, W. AlKawai, M. M. Mezghani, M. I. Marhoon, Y. Yang   +4 more
openaire   +1 more source

CNN-Based Logging Lithology Identification Technique and its Application

82nd EAGE Annual Conference & Exhibition, 2021
Summary Lithology identification is very important in reservoir evaluation, and it is the basis for obtaining the other rock properties in the reservoir. Deep learning has become a popular and reliable method in image classification. Most of the published lithology identification methods based on deep learning use lithology images as the input data ...
W. Xiong, C. Xu, L. Li, S. Zhan, P. Chen, Y. Gao   +5 more
openaire   +1 more source

Lithology identification on well logs by fuzzy inference

Journal of Petroleum Science and Engineering, 2019
Abstract The purpose of this work is to present a fuzzy inference system in order to identify lithologies from wireline logs and core data from a specific borehole and transport this information to nearby uncored wells in the same oilfield. Input variables in this inference system are natural gamma ray log (GR) and porosity logs (density, neutron ...
Diogo Maia Ramos Lopes, André José Neves Andrade   +1 more
openaire   +1 more source

Application of Adaboost-Transformer Algorithm for Lithology Identification Based on Well Logging Data

IEEE Geoscience and Remote Sensing Letters
In the field of oil and gas exploration, accurately predicting lithology during well logging is crucial. This research introduces a novel approach, the Adaboost-Transformer method, which utilizes data mining techniques to enhance logging lithology ...
Youzhuang Sun, Shanchen Pang, Yongan Zhang   +2 more
semanticscholar   +1 more source