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Keywords: neural network
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Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21208-MS
... using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21175-MS
... enables better collaboration and faster decision making. real time system machine learning benchmarking big data data mining upstream oil & gas performance indicator best practice strategic planning and management project management neural network intervention slip-to-slip time...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21238-MS
... of this insert safety valve was conducted and successfully deployed on other three wells. artificial intelligence downhole intervention neural network scientific discovery well intervention production enhancement artificial lift system completion installation and operations creativity...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21249-MS
..., such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data ( Wu et al., 2019 ). The most used algorithm is U-Net ( Ronneberger et al., 2015 ), which can accurately and efficiently provide probability maps of faults. However...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21221-MS
.... Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues. The solution is based on the Artificial Neural Network (ANN...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21299-MS
... generated from real time measurements in offset wells makes machine learning the ideal tool for analysis and comparison. Artificial Neural Network (ANN) is a relatively simple machine learning tool that combines inputs and calculation layers to compute a specified output layer. The ANN is fed over thousands...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21176-MS
.... It is inefficient and inaccurate to identify different types of curves with traditional methods applied to characterize the levels of TZs. In this paper, convolutional neural network (CNN) is applied to construct a classification model for the automatic identification of the levers of TZs. According to the TZs...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21356-MS
.... machine learning oilfield big data calculation result upstream oil & gas well area robust method fluid density artificial intelligence viscosity output layer fluid parameter international petroleum technology conference neural network recent year algorithm predict fluid property...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21350-MS
.... This paper presents a data-driven algorithm, known as Artificial Neural Networks (ANNs), along with time series forecasting that is a well-known statistical technique. Machine learning model trained by a past well performance data such as tubing head pressure (THP), flowing bottom-hole pressure can predict...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21364-MS
... temperature and pressure make applying of deep learning technique to predict flow rate possible. Flow rate of production well is predicted with long short-term memory (LSTM) network using downhole temperature and pressure production data. The specific parameters of LSTM neural network are given, as well...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21425-MS
... optimization under uncertainty. This new workflow is impactful for operators to create robust decision after considering the associated risks. machine learning banking & finance neural network knowledge management asset and portfolio management data-driven multi-asset optimisation contractor...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21389-MS
... fracture information in this oilfield accurately and comprehensively. In order to solve the problems mentioned above, this paper makes full use of logging, geology, seismic and other data to carry out fine characterization of fracture with multi-information fusion fracture modeling based on neural network...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21426-MS
...-grade handphones and intrinsically safe wearable devices. It achieved an average distance error of less than 2 meters in 3D space. artificial intelligence wi-fi health & medicine information real time system neural network localization requirement personnel real time tracking...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19659-MS
... using two AI models namely functional neural networks (FNN) and support vector machine (SVM). The AI models were trained to estimate the TOC based on well log data of gamma ray, deep resistivity, sonic transit time, and bulk formation density, more than 500 datasets of the well logs...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19714-MS
... considerations to data quantity and quality. An automatic alarm triggered by suboptimal combustion is needed. In this paper, we propose a solution that uses a deep neural network that learns from flame videos to define the quality of the combustion. Flame features that help determine the combustion quality...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19706-Abstract
... can determine the well performance without introducing the complexity associated with the numerical approaches. Artificial neural network was utilized to estimate the hydrocarbon production for two types of nonconventional wells; fishbone multilateral and hydraulically fractured horizontal wells...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19804-Abstract
... (from image logs) and data aggregation are areas where Deep Neural Networks have proven efficient, but their accuracy in the field of grain size prediction is still limited, hence not useful yet for an interpreter. Since relevant and complete log data is usually only available on small intervals...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19678-Abstract
... Network For Parameters Determination And Seismic Pattern Detection. Document ID- SEG-2006-2285 Michael P. McCormack ( Arco Oil & Gas Co. ) . Neural Networks In the Petroleum Industry, Document ID SEG-1991-0728 David A. Ford ( Diamond Geoscience Research Corp. ) | Michael C...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19816-Abstract
... wells with positive results. machine learning neural network prediction well logging correlation log analysis workflow chlorite saturation dataset PHIT fraction resistivity log Upstream Oil & Gas Artificial Intelligence GR log predictor conductivity application...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, January 13–15, 2020
Paper Number: IPTC-19864-Abstract
... in a state of the turbine that is forecasted into the future using a long short-term memory (LSTM) network. Second, this forecast is analyzed by a neural network trained to recognize normal healthy behavior in order to identify problem. Third, problems can be diagnosed in some special circumstances...

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