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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209255-MS
...Machine Learning Approach From the perspective of machine learning, there are many anomaly detection and prediction analysis methods for high-dimensional data. These methods focus on statistics and analysis of data in the actual production process from the perspective of big data analysis...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209277-MS
... intelligence machine learning pressure transient analysis production forecasting pressure transient testing deep learning drillstem testing structural geology reservoir characterization neural network upstream oil & gas information rta deep learning model permeability oil reservoir...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209245-MS
... than Case 2. In particular, the changes in variance of the error in Case 1 was much less that for Case 2. enhanced recovery neural network surrogate modeling machine learning sagd upstream oil & gas artificial intelligence steam-assisted gravity drainage case 2 optimization...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209280-MS
... in impermeable shale samples, and shale fabric features. We outline a pathway to greater improvement of resolution. reservoir characterization deep learning machine learning neural network complex reservoir network output output image bentheimer sandstone sample interpolation threshold index...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209266-MS
... of the petroleum engineer. In particular, the introduction of SCADA systems and digital technologies allowed the real-time or relevant-time data collection which enabled close monitoring of ESP's performance and optimization. With the introduction of advanced digital technologies such as Machine Learning...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209269-MS
... and horizontally. Anisotropic elastic properties and stresses lead to more complications for predicting the fracture. This study introduces a comprehensive workflow for fracturing design optimization by applying supervised machine learning. The research also aims to develop an algorithm that can help any shale...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209261-MS
... Abstract Quick and reliable forecasting of production data is still challenging in unconventional plays, even with the variety of modifications proposed to Arps decline curve analysis (DCA). Machine learning revealed promising results when enough samples were accessible to train and validate...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209333-MS
... the wellbore is shut-in. We used an Artificial Neural Network (ANN) and K-Means clustering approach for kick prognosis. We trained these Machine learning models to detect kick symptoms from pressure response and gas evolution data collected between the kick occurrence and the Wellhead. The Artificial Neural...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209332-MS
... of these unconventional reservoirs. energy economics production monitoring reservoir surveillance production forecasting shale oil production control modeling & simulation machine learning artificial intelligence complex reservoir regression model prediction petroleum system hyperbolic method...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209336-MS
... be widely applied to different recovery techniques, and asset classes. machine learning complex reservoir artificial intelligence enhanced recovery steam-assisted gravity drainage thermal method sagd principal component reservoir pressure simulation augmented machine learning approach...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209321-MS
... for different reservoirs in future studies and the field. machine learning pressure transient analysis artificial intelligence production monitoring energy economics reservoir surveillance production control shale gas shale oil drillstem testing oil shale multistage fracturing unconventional...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209325-MS
..., implementation, and interpretation. It serves as a guidance by including numerous field cases and the latest research about tracers in geothermal. chemical tracer tracer test analysis information machine learning upstream oil & gas akin artificial intelligence concentration geothermal reservoir...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209330-MS
... inhibition data mining well integrity corrosion management production chemistry riser corrosion h2s management pipeline corrosion oilfield chemistry subsurface corrosion downstream oil & gas machine learning artificial intelligence neural network retrieved december 24 assessment outlier...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209312-MS
... on the provided data set including basic regression models, tree-based methods, support vector machines, and neural networks. The adopted methods will be discussed in the proceeding section along with their corresponding results. Machine Learning Algorithms Logistic Regression logistic regression...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209313-MS
... for reservoir simulation/modelling study of such reservoir where TEOR is implemented. Hence, an attempt has been made to develop a reliable, accurate, and robust data-driven model for two-phase oil/water relative permeability using the XG-Boost machine learning algorithm which accounts for the temperature's...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 26–28, 2022
Paper Number: SPE-209300-MS
... and reservoir pressure, which can be applied in a practical and automated manner. Traditional surveillance methods are interpretive and do not scale for manual surveillance of either large fields or those with large data volumes. In this work, we propose a machine learning approach to discover physics that can...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 20–22, 2021
Paper Number: SPE-200771-MS
... (Anadarko's SPE 187222 Creating Value by Implementing an Integrated Production Surveillance and Optimization System – An Operator's Perspective and Chevron's SPE 181437 Application of Machine Learning in Transient Surveillance in a Deep-Water Oil Field, are good examples). Required and expected efforts...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 20–22, 2021
Paper Number: SPE-200835-MS
... to evaluate the economics and risk profiles of the new opportunities. A data-driven workflow can facilitate this process and make it less biased by enabling the agnostic analysis of the data as the first step. In this work, several machine learning algorithms are briefly explained and compared in terms...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 20–22, 2021
Paper Number: SPE-200812-MS
... simulation result chemical flooding methods reservoir simulation simulation model model parameter polymer concentration machine learning modeling & simulation average value experiment sp flooding petroleum engineer ensemble oil recovery surfactant concentration enhanced recovery history...
Proceedings Papers

Paper presented at the SPE Western Regional Meeting, April 20–22, 2021
Paper Number: SPE-200766-MS
... Abstract Missing values and incomplete observations can exist in just about ever type of recorded data. With analytical modeling, and machine learning in particular, the quantity and quality of available data is paramount to acquiring reliable results. Within the oil industry alone, priorities...

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