... , Xuebei Shi , and Huiwen Sheng , Halliburton, Bringing Huge Core Analysis Legacy Data Into Life Using Machine Learning , OTC-31419-MS Abstract This paper presents the development and application of COREAI, a machine-learning-based platform designed to manage and analyze Malaysia's...

... by incorporating a multiplier (E-factor) to account for the impact of aquifer strength and improve storage capacity estimation during the early stages of development planning. Machine learning models including Gradient Boosting Regressor, Random Forest Regressor and Linear Regression shows a prediction of 90 per...

... preservation. sustainability social responsibility air emission artificial intelligence machine learning sustainable development compressor back pressure control valve climate change recycle valve valve data mining consumption operation energy consumption chevron thailand exploration...

... 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...

... Abstract 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...

... & 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 model geological subdiscipline sand particle...

... 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 petrophysical data, dynamic production attributes, and production potential using automated calibrated models...

... 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 algorithms are utilized...

.... Additionally, the study explores the potential of integrating tiltmeter data with coupled geomechanical modeling to provide insights into pressure propagation and potential plume movement during CO 2 injection. A 3D geomechanical model was constructed by applying a machine learning technique to a 1D...

... 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...

... control production profile sedimentary rock temperature profile drilling operation rock type reservoir surveillance machine learning model parameter algorithm permeability well trajectory equation vector es-mda method directional drilling flow in porous media production monitoring...

... and gas field is seldom reported. This study examines 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...

... 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...

...) Identify areas that require deeper engineering studies. (2) Facilitate an exchange of local best practices. The next step is to leverage on machine learning and natural language processing technologies. Specifically, the team is now working on how to improve the identifications of connections between...

... machine learning assessment objective vendor reliability efficiency application spare part consequence risk management decision tree probability donggi senoro lng decision tree analysis spherical link decision analysis tool consideration Introduction Key Factor in Achieving World...

... water injection rate of each well and interval is critical for effective and successful water injection. This paper presents a fast and intelligent water injection allocation method based on comprehensive machine learning algorithm. The evaluation indexes of injection performance are established...

... 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 predictive solution to improve sand management strategy decision making. The intelligent sand management...

... vibration and bit wear misjudgment on efficient shale gas drilling. This paper presents the revealing and solving of these two 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...