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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214473-MS
... in this study. The statistical and graphical error analyses were used to check the performance and accuracy of the ANN model and correlation. neural network machine learning upstream oil & gas thermal expansion expansion gravity pvt measurement saudi arabia government correlation empirical...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214462-MS
... ESP run life. artificial intelligence upstream oil & gas esp asia government artificial lift system pfa operator electrical submersible pump failure rul pred abu dhabi international petroleum exhibition united states government machine learning conference information graph...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214475-MS
... can produce a significant million standard barrels more oil than the original development scenario within three years. This technology eliminates limitations for multiple scenario assessments. In our AI hybrid model, the power of dynamic reservoir simulation is combined with a modern machine learning...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214459-MS
... approach for resources (particularly emissions, energy, water, waste, materials,, and safety) This was achieved through a combination of AI methods such as unsupervised machine learning, multi-variate optimization, and the implementation of similarity measures. A few of the inputs included well data...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214478-MS
... management machine learning subsurface application engineer example interpreter well logging workflow software naming convention petroleum engineer Introduction Problem Outline Disciplines like Petrophysicist, Geologists, Geophysicists, and Reservoir engineers have specific workflows...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214460-MS
..., researchers developed several machine learning models based on well-logging data to avoid challenges associated with direct lithological identification and increase identification accuracy. Nevertheless, high uncertainty and low accuracy are commonly encountered issues due to the heterogeneous nature...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214461-MS
... is not worse than one demonstrated by commonly used flow simulators, while being less computationally expensive. upstream oil & gas artificial intelligence pinn deep learning prediction asia government machine learning coefficient architecture neural network crm physics-informed neural...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214466-MS
.... united states government asia government saudi arabia government upstream oil & gas square method equation artificial intelligence curve equation drilling performance predict drilling performance knowledge management drilling performance curve drilling operation machine learning...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214468-MS
... uniaxial compressive strength. The supervised machine-learning models are trained on input variables such as weight on bit, torque, rotational speed, uniaxial compressive strength, vibrations, and more importantly the measured force at the PDC cutter. A physics constraint is applied for torque, weight...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214465-MS
... intelligence reinforcement learning sequence algorithm automation decision-making information epoch upstream oil & gas drilling equipment machine learning evaluation spe-214465-ms markov decision process transition probability real-time autonomous decision-making effect reinforcement...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214457-MS
... the machine learning model design. This type of analysis can also be used in future phases to provide quality assurance of forecast data coupled with drilling data. Basket Data Analysis Basket data contains the predefined requirements lists for a specific well design. The baskets follow iteration...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214476-MS
... characterization application algorithm winland well logging machine learning multivariate gaussian regression interpretation permeability united states government modeling & simulation log analysis dataset correlation paper ppb workflow sedimentary rock geologist shf similarity clastic...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208661-MS
.... Based on the bench test results, we investigated the possibility of indirect determination of well operation parameters during artificial lift modelling by machine learning. As a result, the approaches to modelling taking into account various sets of parameters (features) have been studied: based...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208642-MS
... a guide for the formation storage capacity and interpretation analysis. well logging drilling operation machine learning fuzzy logic log analysis neural network artificial intelligence upstream oil & gas abdulraheem accuracy drilling complex lithology prediction drilling data...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208663-MS
... analysis neural network classifier log data reservoir characterization fuzzy logic correlation evaluation resistivity shale layer pseudo cgr log neuron anfis membership function petroleum engineer machine learning artificial intelligence adaptive neuro-fuzzy inference system artificial...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208634-MS
... Abstract A practical data science, machine learning, or artificial intelligence project benefits from various organizational and managerial prerequisites. The effective collaboration between various data scientists and domain experts is perhaps the most important, which is discussed here. Based...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208638-MS
... of acceptance in order to succeed. Proper onboarding and user acceptance is requisite to proper system configuration and performance. This paper sets forth guidelines that can be considered standard for initiating an AI drilling program. machine learning upstream oil & gas window size roadmap...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208667-MS
... of gas viscosity of Yemeni gas fields using machine learning techniques. Performance of some machine learning techniques in the prediction of gas viscosity investigated in this work. The techniques include K-nearest neighbors (KNN), Random Forest (RF), Multiple Linear Regression (MLR), and Decision Tree...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208648-MS
...) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven...

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