... safety knowledge management adnoc international petroleum exhibition & conference human factor incident investigation oil and ga industry tree analysis incident investigation process machine learning investigation process incident investigation methodology abu dhabi international...

... timesteps, not perfect match at well level, etc. The objective of this study is to develop a combined AI and physics driven method for fast and robust procedure to estimate reservoir pressure in space and time, based on a hybrid approach using production injection data, machine learning and material...

... needs and conditions. These include production data, well logs, and seismic data, among others. This data is utilized to construct a resilient proxy model utilizing machine learning algorithms, leading to precise predictions of field water production. Moreover, these runs are completed in a matter...

... that can be efficiently extracted ( Tahmasebi et al ., 2017 ). Abstract This study leverages wavelet analysis and machine learning (ML) techniques, including a 1D Convolutional Neural Network (1D CNN), to analyze inter-well connectivity and pinpoint an optimal new drilling location (sweet spot...

... and, with the integration of machine learning algorithms, have been extended to applications in gas, gas-alternating-water injection, and other enhanced oil recovery mechanisms. In this paper, we introduce a novel data-driven formulation that reduces the number of optimization parameters by 50% compared to traditional CRM...

... analytics. Unlike physical flow meters, VFMs do not require direct measurement of fluid flow. Instead, they rely on a combination of real-time sensor data, production test results, and machine learning algorithms to estimate daily production rates of gas, condensate, and water. This approach offers a range...

... subsurface storage machine learning deep learning artificial intelligence expert system pressure data inverse problem injection well evolution saline aquifer neural network model spatial distribution forward-looking model plume gas plume inverse model workflow permeability permeability...

.... Recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) have shown promise in enhancing data reliability and real-time lithology prediction. The early explorations by Rogers et al. (1992) , Benaouda et al. (1999) , and Wang and Zhang (2008) laid the groundwork, utilizing well...

... Driving the Need for Change Several tailwinds are propelling the necessity for a micro-credentials-based curriculum in petroleum engineering: Technological Advancements: Rapid advancements in technology, including artificial intelligence, machine learning, and automation, are revolutionizing...

.... , V. Kaynig , C. Rueden , K.W. Eliceiri , J. Schindelin , A. Cardona & H. Sebastian Seung ( 2017 ) Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification . Bioinformatics , 33 , 2424 – 2426 . Davies , R. , G. Foulger...

... , Arévalo et al. 2022 ), among others, have prepared the ground for a more accurate detection of downhole hazards, such as conditions leading to stuck pipe. This paper presents the utilization of machine learning (ML) to analyze surface data, detect stuck pipe events and classify such events into categories...

... petrophysical evaluation result lattice evaluation result machine learning microporous rock type micp data reservoir property water saturation Introduction Microporous carbonates represent a vast part of Thamama group reservoirs. The subjects of study are two Thamama group reservoirs which...

... Key Components Integration of Subsurface Workflows The agile workflow integrates various subsurface workflows, enabling the generation of multiple geological realizations and reservoir simulations. This integration is powered by high-performance computing and machine learning solutions, which...

... Abstract There are various types of available data, with different data structures, dimensions, and intervals. Conventional big data algorithms can lead to poor interpretability. In this study, novel methods are proposed based on big data analysis and machine learning. First, the data...

... to revolutionize the efficiency, safety, and sustainability of maritime operations, presenting transformative insights for the industry's advancement. machine learning artificial intelligence efficiency maritime operation maritime industry operation safety value chain maintenance sustainability...

... also lead to insufficient injection rate, post-breakdown, to place proppant. A machine-learning (ML) model based on in-depth multidomain analysis can assist in such cases in the design and execution phase. Part I of the paper here covers the extensive ML modeling. The following Part II will cover...

... operator and transfer learning fno model machine learning neural operator transfer learning computational cost dataset fourier neural operator saturation prediction operational condition INTRODUCTION The growing concern over climate change has led to an increased focus on strategies...

... critical issues, and capture knowledge for future wells, all while overcoming the challenges of accessing and interpreting large SQL databases. deep learning large language model natural language machine learning artificial intelligence sql query engineer empowering drilling and optimization...

... graph with the help from a LLM by augmenting its capabilities using contextual information. Retrieval Augmented Generation Based LLM prompting for Causal Graph Data-driven process management coupled with machine learning have been successful in driving commercial value to oil and gas operators...

... operation machine learning natural language decision-making abu dhabi international petroleum exhibition engineer international petroleum exhibition & conference drilling database application drilling report dataset query generative ai Introduction Drilling industry in oil and gas...