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Keywords: machine learning
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Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21305-MS
... considerations. machine learning upstream oil & gas surrogate component crude oil artificial intelligence eacn acid pvt measurement enhanced recovery molecule linear alkane alkane study field digital oil model equivalent alkane carbon number surfactant international petroleum technology...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21310-MS
... obtained during evaluation. This has helped reduce potential risks and improve reliability and performance, which can act as best practices and can be applied within similar fields. machine learning sand/solids control hydraulic fracturing artificial intelligence unconsolidated well multistack...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21312-MS
... data and algorithm. The variegated outcomes observed from some of the AI and analytics tools studied in this research shows that, when it comes to adopting AI and analytics, the worm remains buried in the apple. data mining sustainable development machine learning artificial intelligence...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21356-MS
... methods are very large. Therefore, how to quickly and accurately obtain fluid physical properties is of great significance. In recent years, with the development and improvement of artificial intelligence or machine learning algorithms, their applications in the oilfield have become more and more...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21364-MS
... performance of flow rate prediction than other five machine learning methods, including support vector machine (SVM), linear regression, tree, and Gaussian process regression. The LSTM with a dropout layer has a better performance than a standard LSTM network. The optimal numbers of LSTM layers and hidden...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21317-MS
... water flooding reservoirs. production control machine learning evolutionary algorithm artificial intelligence enhanced recovery oilfield upstream oil & gas prediction connectivity coefficient reservoir surveillance predict water flooding performance multi-layer reservoir production...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21357-MS
... reservoir fluid samples were studied in the laboratory. Chemical tracers contained in the samples were detected by flow cytofluorometry using custom-tailored machine learning-based software. The studies helped identify the productivity of each frac port, calculate the contribution of each port in percentage...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21350-MS
... 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-21323-MS
... techniques. reservoir surveillance machine learning artificial intelligence reservoir simulation production monitoring modeling & simulation production control risk management upstream oil & gas uncertain variable simulation run order interaction term variability workflow order...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21425-MS
...-dependent, multi-asset 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...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21436-MS
... prediction that combines rock-types, flow-zone-indicator (FZI), and machine-learning techniques (ML). FZI is a reservoir-flow-unit that controls hydraulic fluid-flow and is influenced by pore-geometry resulting from diagenetic-processes. In reservoirs, pore-geometry usually is heterogenous due to mineral...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21389-MS
... modeling geologic modeling machine learning artificial intelligence fracture characterization fracture network model well logging neural network oilfield fracture development international petroleum technology conference reservoir characterization hydraulic fracturing log analysis upstream...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21426-MS
... the development and deployment of an end-to-end cost-effective real-time personnel location system suitable for both indoor and outdoor hazardous and safe areas. It leverages on facility wireless communication systems, wearable technologies such as smart helmets and wearable tags, and machine learning...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21202-MS
... project. The operating model was built by machine learning using various historical data recorded in PI system, records of maintenance data and other relevant information such as manufacturer manual, international standard and related white papers. The modelled algorithm was embedded in an application...
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-21176-MS
.... artificial intelligence enhanced recovery chemical tracer complex reservoir machine learning neural network upstream oil & gas recall 0 waterflooding accuracy classification result tracer breakthrough curve deep learning ddp-cnn precision 0 cnn model activation function training sample...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21205-MS
..., automatic dipole-flexural shear extraction is done using physics-based machine learning (ML) where purely data-driven models are inadequate due to borehole or geological conditions. The physics-based ML utilizes cloud-based computing that is needed for large volume synthetic data generation and neural...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21199-MS
... intervention and remediation action. Over the decades, multiple analytical studies have been attempted to predict stuck pipe occurrences. The latest venture into this drilling operational challenge now utilizes Machine Learning (ML) algorithms in forecasting stuck pipe risk. An ML solution namely, Wells...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, March 23–April 1, 2021
Paper Number: IPTC-21164-MS
... uncertainty quantification or optimization). In this paper, we develop a machine learning assisted relative permeability upscaling procedure, in which the full numerical upscaling is performed for only a portion of the coarse blocks, while the upscaled functions for the rest of the coarse blocks are...

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