... to continued collaboration in future endeavors. Abstract This paper presents the development and application of COREAI, a machine-learning-based platform designed to manage and analyze Malaysia's extensive legacy petroleum engineering core data. The platform transforms data from 23,000 core plugs across...

... simulation social responsibility reservoir surveillance production monitoring machine learning storage capacity injection different case aquifer case strong aquifer case gas cumulative production gas reservoir gas production ahmad khanifar correlation raj deo tewari production reservoir...

... plants in the oil and gas industry. The strategy involves an innovative tool that predicts the short-term trend of the energy efficiency index and guides optimal management of an oil and gas production plant. The tool integrates Big Data Analytics and Machine Learning (ML) methodologies with expert...

... Abstract This study introduces an innovative framework for harnessing Machine Learning (ML) within production engineering. The objective is to offer engineers a comprehensive framework for utilising ML-based modelling across core production engineering tasks to elevate operational efficiency...

... The ability to identify, and manage unexpected events is essential for improving productivity and minimizing downtime. Recently, there have been significant advancements in statistical and machine learning techniques for anomaly detection. Despite this, aging and increased downtime issues have been observed...

... clastic rock rock type modeling & simulation sand control size distribution sandstone sand/solids control geology artificial intelligence reservoir geomechanics machine learning wellbore formation sand coefficient particle size distribution sand particle sand production prediction...

... potential by deploying logging tools downhole may be challenging in some fields, especially those with highly deviated or horizontal wells. A novel BCO identification process discussed in this paper leverages Machine Learning (ML) model within an Artificial Intelligence (AI) framework that integrates...

...-to-use tool to automatically detect stuck pipe accurately and early. Based on the in-depth theoretical analysis and historical stuck pipe data analysis, main early stuck pipe indicators during different drill operations are identified. More than ten time series analysis algorithms and machine learning...

...-dimensional (3D) geomechanical model was constructed by integrating reservoir models with well data. Machine learning techniques were utilized to create a three-dimensional distribution of geomechanical properties, capturing their heterogeneity. Following the construction of the 3D geomechanical model...

... reservoir simulation modeling & simulation structural geology flow in porous media geological subdiscipline underground hydrogen storage injection storage container machine learning hydrogen storage cushion ga tarata thrust gas injection tear fault model new zealand aotearoa new zealand...

... enhanced recovery oil recovery incremental oil recovery relative error climate change interaction feature interaction optimization rf proxy model rock type chemical flooding methods proxy model model output explanation numerical model artificial intelligence machine learning reservoir...

... algorithms, face efficiency issues due to their slower speed in the inversion process. With the continuous growth of computation and data processing technologies, machine learning algorithms and data assimilation methods are evolving ( Chen and Oliver, 2010 ; Feng et al., 2018 ; Wang et al., 2022b...

... the application of statistical and machine learning methods for causality analysis in the oil and gas industry. It includes various use cases to demonstrate the effectiveness of these approaches. This study investigates causality analysis techniques and their applications through comprehensive literature review...

... methods of differential flow and flow-back fingerprint. well control detection algorithm presented artificial intelligence drilling conference annular pressure drilling machine learning exhibition kick detection threshold false alarm displacement flowback fingerprint iadc spe drilling...

... these candidates has decreased, leading to a decline in the quality of workover outcomes. To tackle this issue, implementing machine learning aims to maintain the massive of workover jobs while ensuring the accuracy of perforation candidate results. This study utilizes 12,247 cleansed historical workover data...

... on machine learning and natural language processing technologies. Specifically, the team is now working on how to improve the identifications of connections between contents: single entries that may appear insignificant, once connected to others, show the development of weak signals that need to be addressed...

... gas (lng) decision support system artificial intelligence decision analysis decision-making process machine learning assessment objective vendor reliability efficiency application spare part consequence risk management decision tree probability donggi senoro lng decision tree...

... machine learning algorithm. The evaluation indexes of injection performance are established by integrating dynamic production data, such as injection-production ratio, water consumption rate, water cut, injection intensity, daily oil production capacity. According to the evaluation indexes...

... and data science teams to address the oil and gas wells sanding pain points. Leveraging on its local regulatory advantage, consisting of petroleum engineering expertise and mass data from all the operating fields, the digitalisation of offset data into machine learning (ML) has enabled the nation's...

... hidden hinders based on data analytics and machine learning. Various sources of data, including over 300 cases of bit records, lithology, elemental logging and downhole vibration measurements from drilled formation for selected representative drill bits, as well as the data from full-scale indoor...