... systems for emulsion monitoring, and even downhole drones, as well as several optimization works using artificial intelligence and machine learning, for quality and control. In the materials-science aspect, composite and nonmetallic materials continue to be significantly researched, as well as additive...

..., slurry flow modeling, production analysis, and machine learning. The new digital framework proposes solutions for the limitations of current methodology. Introduction Across different generations, multiple fluid additives have been introduced to target specific treatment objectives; one of these that has...

... the predicted value and experimental data. 1 11 2023 1 11 2023 1 11 2023 1 11 2023 Copyright 2023, Offshore Technology Conference upstream oil & gas europe government fraction multiphase flow reservoir surveillance production monitoring machine learning artificial...

...Chris Carpenter _ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 211772, “Optimizing Future Drilling-Center Locations Using Advanced Analytics and Machine-Learning Algorithms Offshore Abu Dhabi,” by Rail Salimov, Benoit Jaffres, and Jamal Alblooshi...

... jpt.spe.org 103 neural network deep learning enhance transfer learning deep learning machine learning upstream oil & gas artificial intelligence tight-oil production prediction united states government correlation reservoir simulator deep-learning architecture unconventional reservoir...

..., and post-FH cleanout are effective techniques to mitigate production loss. JPT jpt.spe.org 97 upstream oil & gas artificial intelligence wellbore eagle ford fracture machine learning mitigation refracturing geologist hydraulic fracturing geological subdiscipline prediction completion...

... on fundamental assumptions that may or may not be correct in all situations but are intended to represent reservoir behavior. While data-driven models do not make any assumptions about underlying physics, they rely on training data. Therefore, data analytics and machine learning can be an ideal substitute. Local...

... operation and facilitates educated decision-making. Hence, despite the thrilling advancements in deep learning and large language models, the upstream industry continues to extensively use traditional machine-learning models such as tree-based algorithms for processing structured data sets because...

...Reza Garmeh The integration of machine learning and physics-based simulation modeling offers a comprehensive approach to optimizing unconventional development and addressing parent-/child-well depletion issues. By combining the predictive capabilities of machine-learning models with the insights...

...Chris Carpenter _ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213869, “Water Digital Avatar—Where Chemistry Is Mixed With Machine Learning,” by Jesse Farrell, SPE, and Sergey Makarychev-Mikhailov, SPE, SLB. The paper has not been peer reviewed...

... signals to production or injection-rate data. Signal-to-noise ratio should be considered in the calculations, and (deep) machine-learning methods should be employed. Most of the publications in the literature are 56 JPT | September 2023 TECHNICAL PAPERS | Reservoir Surveillance qualitative, especially...

...Chris Carpenter _ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3719366, “Integration of Machine Learning and Numerical Simulation To Estimate Child-Well Depletion,” by Edward Wolfram, SPE, James Cassanelli, and Soodabeh Esmaili, SPE, Occidental...

... proxy model forecast production geology geologist deep learning rock type machine learning hyperparameter neural network shale gas mechanism att-gru shale reservoir requirement complex reservoir artificial intelligence complex fracture network bhp simulation mudrock mudstone shale...

... 2023 1 7 2023 1 7 2023 Copyright 2021, Society of Petroleum Engineers asia government modeling & simulation pvt measurement gas injection gas injection method kazakhstan government application machine learning enhanced recovery upstream oil & gas optimization...

...; and Hussein Mustapha, Schlumberger, et al. The paper has not been peer reviewed. _ To unlock the potential of large relative permeability ( K r ) databases, the work flow proposed in the complete paper integrates data analysis, machine learning (ML), and artificial intelligence (AI). The work flow allows...

... paper in the feature, SPE 206340, deals with stuck-pipe incidents, a major cause of economic losses, safety hazards, and nonproductive time. With the use of artificial intelligence (AI) and machine learning (ML) techniques, however, these incidents can be prevented by analyzing drilling data. In recent...

... 1 7 2023 Copyright 2021, Society of Petroleum Engineers upstream oil & gas information removal kick artificial intelligence classifier linear regression node machine learning node network drilling memo categorize algorithm natural language processing increase accuracy...

...Chris Carpenter _ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22188, “Machine-Learning-Empowered Well Placement in a Large Unconventional Tight Gas Field in China,” by Ting Yu, SLB; Xiangzeng Wang, Shaanxi Yanchang Petroleum; and Alexis...

... address a solution to this problem wherein a machine-learning (ML) tool is proposed to augment viscosity measurements by calculating the viscosity using field data for the periods when no viscosity measurements are available. 1 6 2023 1 6 2023 1 6 2023 1 6 2023 Copyright 2022...

... 6 2023 1 6 2023 Copyright 2022, Society of Petroleum Engineers artificial intelligence breakdown effectiveness hydraulic fracturing hydraulic fracturing operation consideration optimization problem machine learning knowledge algorithm basin signature...