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Keywords: neural network
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Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-198972-MS
... of a geometry sticking event. Artificial Neural Networks (ANN) represents a category of machine learning models, specifically, in the supervised learning area, which are based on the model of perceptron. The computational model is inspired by the studies of the central nervous system of mammals...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199005-MS
... process is time consuming and alternatives such as data-driven proxy modeling can overcome the computation complexity drawbacks. A machine learning technique called recurrent neural network (RNN) has been proved useful for reservoir modeling with sequence data. In this paper, we develop a novel end-to-end...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199047-MS
... artificial intelligence upstream oil & gas machine learning bitumen accuracy optimal eor method selection oil sand complex reservoir early production forecasting prediction workflow evaluation genetic algorithm enhanced recovery neural network heavy oil field screening...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199042-MS
... (QoI) like oil production rates and water cut. Thus, the error correction model proposed using Artificial Neural Networks (ANN) that considers the physics based reduced model solutions as features, proved to reduce the error in QoI significantly. Speed-ups of about 50x-100x were observed for different...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199082-MS
... in the Duvernay reservoir by introducing a methodology based on geological characterization, and neural network analysis on more than 200 wells, evaluating production performance, hydraulic fracturing job, and completion efficiency. In this paper, three machine learning algorithms and approaches...
Proceedings Papers

Paper presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, May 17–19, 2017
Paper Number: SPE-185536-MS
... ] ∈ ℝ r × n An Artificial Neural Network (ANN) is a computational model, inspired in the structure of the biological brain, used to solve problems of classification and regression. It consists in a large connection of processing units, called artificial neurons, grouped in multiple...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, November 18–20, 2015
Paper Number: SPE-177086-MS
... classification using only well logs interpretation techniques, has its limits. In this paper, we introduce a rock type neural network technique based on Indexed and Probabilistic Self-Organized Mapping (IPSOM) which was designed for the geological interpretation of well log data, facies prediction and optimal...
Proceedings Papers

Paper presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, May 21–23, 2014
Paper Number: SPE-169388-MS
... reasons may sharply limit the carrying out of an optimal formation characterization methodology along the entire productive or injective lifespan of a reservoir. Nowadays, artificial neural networks (ANN) are one of the strongest tools to supply such missing information in order to generate synthetic logs...
Proceedings Papers

Paper presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, April 16–18, 2012
Paper Number: SPE-153446-MS
.... A solution based on Artificial Neural Networks (ANN) can be used for this task due to their ability to divide the representative space of attributes obtained in logs (input parameters for the network) into areas that represent the different fluids, even if this division is non-linear. Abstract...
Proceedings Papers

Paper presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, April 16–18, 2012
Paper Number: SPE-153908-MS
... of the well and reservoir, plus specific yet to be characterized gel kinetics parameters. In this work a model is proposed to predict a well future production response based on well and reservoir information that is commonly available. Neural network models were designed, trained and validated for predicting...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, December 1–3, 2010
Paper Number: SPE-139135-MS
... the drilling fluid used is water-based mud. The artificial neural networks (ANN) have traditionally been used to correct well log data corrupted by tension pulls on the tool or degraded by poor borehole conditions. They have also been used for cases in which it is not feasible to obtain a traditional set...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, December 1–3, 2010
Paper Number: SPE-139147-MS
... Abstract In this paper, Artificial Neural Networks (ANN) are used to predict the bottom-hole flowing pressure in vertical multiphase flow. Two-phase flow of gas and liquids is commonly encountered in the production and transportation of oil and gas. Knowing the bottom-hole pressure (BHP...
Proceedings Papers

Paper presented at the Latin American and Caribbean Petroleum Engineering Conference, May 31–June 30, 2009
Paper Number: SPE-122826-MS
... or not) and Artificial Neural Network. A simple synthetic case (PUNQ), and a real complex case (Brazilian onshore field) were used to illustrate the functional approach. Introduction The main motivation for this work is to discuss the history matching process in the way it is being applied in most cases. Despite many...
Proceedings Papers

Paper presented at the Latin American and Caribbean Petroleum Engineering Conference, May 31–June 30, 2009
Paper Number: SPE-122148-MS
... of simulation runs at the same time but cannot address and cannot solve the problem in a proper way. This paper presents an alternative proposition to speed up the history matching process: the application of feed-forward neural networks as nonlinear proxies of reservoir simulation. Neural networks can map...
Proceedings Papers

Paper presented at the Latin American & Caribbean Petroleum Engineering Conference, April 15–18, 2007
Paper Number: SPE-107468-MS
... Abstract Artificial neural networks are becoming increasingly popular in the oil and gas industry. In the past, studies have been done on the use of artificial neural networks in reservoir characterization, field development and formation damage prediction, to name a few. The aim of this study...
Proceedings Papers

Paper presented at the Latin American & Caribbean Petroleum Engineering Conference, April 15–18, 2007
Paper Number: SPE-107527-MS
... analysis machine learning application well neutron data pulsed neutron data spe 107527 permeability Upstream Oil & Gas training well application resistivity Mexico neural network information evaluation modeling operation openhole data interpretation pulsed neutron tool reservoir...
Proceedings Papers

Paper presented at the Latin American & Caribbean Petroleum Engineering Conference, April 15–18, 2007
Paper Number: SPE-107716-MS
... SPE 107716 Neural Network and 3D Seismic Techniques Improve the Prediction of Facies Distribution Within a Submarine Channel Complex: Neuquen Basin, Argentina F. Raggio and A. Ortin, Repsol YPF, and C. Martinez and P. Uzzo, Schlumberger Copyright 2007, Society of Petroleum Engineers This paper...
Proceedings Papers

Paper presented at the Latin American & Caribbean Petroleum Engineering Conference, April 15–18, 2007
Paper Number: SPE-107909-MS
... machine learning fuzzy logic network prediction correlation Permeability prediction flow in porous media Fluid Dynamics field data artificial neural network Artificial Intelligence Upstream Oil & Gas irreducible water saturation porosity neural network permeability value...
Proceedings Papers

Paper presented at the Latin American & Caribbean Petroleum Engineering Conference, April 15–18, 2007
Paper Number: SPE-107371-MS
... Artificial Intelligence radial basis function network predictive data-mining technology machine learning Optimal Search information Upstream Oil & Gas reasonable performance neural network oil-production prediction prediction proceedings specific knowledge production history...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, June 20–23, 2005
Paper Number: SPE-94780-MS
...-data quantification, integration, statistical and visual analysis, and development of a predictive artificial neural network (ANN) model capable of identifying sands with commercial hydrocarbon potential. The ANN model was used to identify patterns and trends related to the geology, reservoir, well...

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