Results 231 to 240 of about 4,702 (274)
Some of the next articles are maybe not open access.
Application of specific energy for lithology identification
Journal of Petroleum Science and Engineering, 2020Abstract The previous applications of specific energy to drilling operations have focused mainly on drilling optimization and identification of inefficient drilling conditions. Recent advances in specific energy extend its applications to overpressure detection and pore pressure prediction.
Olalere Oloruntobi, Stephen Butt
openaire +1 more source
Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances [PDF]
Journal of Petroleum Science and Engineering, 2018 Identification of underground formation lithology from well log data is an important task in petroleum exploration and engineering. Recently, several computational algorithms have been used for lithology identification to improve the prediction accuracy.
Yunxin Xie, Chenyang Zhu
exaly +2 more sources
Identification of lithology in the Gulf of Mexico
The Leading Edge, 1998In 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 +4 more
openaire +1 more source
Lithology identification by AVO inversion
SEG Technical Program Expanded Abstracts 1995, 1995A stratigraphic elastic inversion scheme has been applied The main assumptions in the present scheme are: (i) The to two data sets from the ‘Troll East field. The objective of model is locally close to horizontally layered (i.e. the model the present work is to obtain estimates of Pand Swave layers exhibit small dips and small lateral velocity ...
Arild Buland +4 more
openaire +1 more source
Decision Tree Ensembles for Automatic Identification of Lithology
SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, 2023Abstract Lithology types identification is one of the processes geoscientists rely on to understand the subsurface formations and better evaluate the quality of reservoirs and aquifers. However, direct lithological identification processes usually require more effort and time.
Mahmoud Desouky +2 more
openaire +1 more source
Lithology Identification Using Lithology Impedance in Mumbai Offshore
2019The study area belongs to the marine geological setup of Surat depression of Mumbai Offshore Basin, India. The lithology of three wells in Mumbai offshore is analyzed using Gamma-Ray (GR) log, Density (ρ) log, Resistivity (Rt) log and Neutron Porosity (Φ) log. This analysis makes the identification of the lithology difficult.
Amrita Roy, Rima Chatterjee
openaire +1 more source
Carbonate Lithology Identification with Machine Learning
Abu Dhabi International Petroleum Exhibition & Conference, 2019Abstract Machine learning has attracted the attention of geoscientists over the years. In particular, image analysis via machine learning has promise for application to exploration and production technologies. Demands have grown for the automation of carbonate lithology identification to shorten the delivery time of work and to enable ...
Takashi Nanjo, Satoru Tanaka
openaire +1 more source
Application of Active Learning in Carbonate Lithologic Identification
2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 2021In the process of oil and gas reservoir exploration, the high cost of lithology information obtained by core drilling makes it difficult to collect a large number of lithology samples. It is the key to use a small number of labeled samples with lithology information to train lithologic identification models. This paper proposes to apply active learning(
Biao Yuan +4 more
openaire +1 more source
Lithologic character identification based on QPSO-SVM
2012 IEEE 11th International Conference on Signal Processing, 2012A novel support vector machine based on quantum particle swarm optimization (QPSO-SVM) is proposed for better solving formation lithologic character identification problem. An identification model for formation lithologic character is established using the data of actual well logging and lithologic profile by training the SVM, which is optimized by ...
Wuli Wang, Hai Ma, Yanjiang Wang
openaire +1 more source
Auto-Lithology Identification Based on KNN
Advances in Science and TechnologyAccurately grasping the distribution of different rocks can achieve the minimum explosive consumption and meet with the requirements of blasting quality; Not only reduces the cost of blasting, but also improves the safety and controllability of blasting.
Wen Xin Ji +3 more
openaire +1 more source