Results 271 to 280 of about 3,766,928 (338)

Constraining Reservoir Quality Predictions

Proceedings, 2012
An inverse rock-physics modelling strategy was used to provide estimates of reservoir parameters. Reservoir predictions made irrespectively of their spatial location, can have dissimilar ranges of variability. For instance, estimations of reservoir quality independently of their depths produced wide ranges of solutions, e.g.
B. Moyano, E. H. Jensen, T. A. Johansen
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Reservoir Souring Prediction in Deepwater Reservoirs for Field Development Planning

SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, 2021
Abstract A deep-water Field X with two major Reservoirs U and L discovered recently offshore Malaysia is on development for early production. The subsurface plan for the Field X includes water injection. But the presence of sulphate rich seawater can provide a favorable environment for souring activity to take place. This study evaluates
Mohd Azri Hanifah, Sai Ravindra Panuganti, Nur Atiqah Zakaria, Nur Hazrina Kamarul Zaman, Raj Deo Tewari   +4 more
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Research on reservoir lithology prediction method based on convolutional recurrent neural network

Computers & electrical engineering, 2021
Considering that conventional reservoir prediction methods cannot fully explore the implicit relationship between seismic attributes and reservoir lithology, a deep learning lithology prediction model combining convolutional neural network and Long Short-
Kewen Li, Yingjie Xi, Zhaoxin Su, Jianbing Zhu, Baosan Wang   +4 more
semanticscholar   +1 more source

Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models

Journal of Petroleum Science and Engineering, 2020
The advent of Artificial Intelligence (AI) in the petroleum industry has seen an increase in its use in exploration, development, production, reservoir engineering and management planning to accelerate decision making, reduce cost and time.
Daniel Asante Otchere, T. Ganat, R. Gholami, S. Ridha   +3 more
semanticscholar   +1 more source

High-Resolution Reservoir Prediction Using Convolutional Neural Networks

81st EAGE Conference and Exhibition 2019, 2019
Summary We propose a new method to predict the reservoir distribution based on convolutional neural networks by using a few well log data. Deep learning methods usually need a large amount of samples, while the well log data is usually limited.
P. Xu, W. Lu, J. Tang, L. Chen
semanticscholar   +1 more source

Software for Reservoir Performance Prediction

SPE Nigeria Annual International Conference and Exhibition, 2015
Abstract Predicting the performance of reservoirs helps engineers to estimate reserve, development planning which requires detailed understanding of the reservoir characteristics and production operations optimization and more importantly, to develop a mathematical model that will adequately depict the physical processes occurring in the
Okotie Sylvester, Onyekonwu M.O.
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Hybrid deep neural networks for reservoir production prediction

Journal of Petroleum Science and Engineering, 2021
For production prediction, existing physics-based methods often depend on some hypotheses and are confined to certain types of reservoirs. Some data-driven methods take single-source or single-type data as inputs and discard the spatial correlation of ...
Zhenyu Yuan, Handong Huang, Yuxin Jiang, Jingjing Li   +3 more
semanticscholar   +1 more source

Reservoir Performance Prediction

2018
Reservoir performance prediction is a key aspect of the oil and gas field development planning and reserves estimation which depicts the behaviour of the reservoir in the future; its success is dependent on accurate description of the reservoir rock properties, fluid properties, rock-fluid properties and flow performance.
Sylvester Okotie, Bibobra Ikporo
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Trends in Reservoir Performance Prediction

Proceedings of SPE Annual Technical Conference and Exhibition, 1994
ABSTRACT Summary Stronger links between geoscience and petroleum engineering are being fostered by new tools and organisations. These linkages are improving the effectiveness of business decisions concerning reservoir performance, and are generating new challenges for the next generation of tools
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Reservoir quality prediction with CSEM

First Break, 2017
CSEM sensitivity to buried resistors is a function of the area, thickness, and resistivity of the resistive body. CSEM information in an exploration process will therefore generally imply changes to one or more of a prospect’s area, thickness, and resistivity expectation.
Daniel Baltar, Neville Barker
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