...," and the SE4All Initiative has mobilized broad international support for achieving universal energy access with good chances to make energy access an explicit Millennium Development Goal (MDG) in the future (UN 2013, UN SDSN 2013). Introduction sustainability machine learning investment social...

... This paper has provided a thorough review of the machine learning and time series forecasting techniques used for oil production analysis. The model performance metrics used to evaluate the quality of the forecast were also explained, and the results were presented and discussed. (1) φ...

.... Through comprehensive exploratory data analysis, this study unveils significant findings that underscore the influence of vehicle characteristics and fuel types within its scope and tackling the complex problem of modeling environmental efficiency with robust algorithms. In this work, machine learning...

... Abstract This study evaluates the effectiveness of machine learning-based time series models as alternatives to short-term traditional decline curve models for estimating hydrocarbon reserves. To accurately estimate the hydrocarbons that can be economically recovered from a field, area...

... that are contingent upon the current market conditions. geologist geology heavy oil play machine learning polymer surfactant produced unconventional play petroleum play type chemical flooding methods injection mobility efficiency reservoir artificial intelligence evolutionary algorithm viscosity...

... performance, contributing to both profitability and sustainability. expert system operation big data artificial intelligence machine learning database algorithm data mining prediction efficiency natural language data science pipeline downstream programming language information petroleum...

... Google Scholar Blockeel , H. , De Raedt , L. and Ramon , J. ( 1998 ). Top-down induction of clustering trees . In J.W., Shavlik (ed.), Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998) , pp. 55 – 63 . Morgan Kaufmann . 103 , 156Google...

... and acceptable accuracy. equation completion installation and operations drillstem testing neural network reservoir surveillance production control drawdown production monitoring dataset prediction drillstem/well testing artificial intelligence machine learning application society university...

... natural gas (lng) machine learning artificial intelligence education engineering student instruction programme holder engineering entrepreneurship information intention student competency entrepreneurship education nigeria participation authorise curriculum Introduction...

... change the likelihood of certain risks. LITERATURE REVIEW geologist geology sedimentary rock modeling & simulation artificial intelligence log analysis enhanced recovery machine learning saturation rock type co 2 reservoir characterization subsurface storage well logging...

... of the collaboration, we now present a framework for the incorporation of data science education into petroleum engineering undergraduate programs. In line with Outcome-based Education approach, a set of learning outcomes for a proposed petroleum data analytics and machine learning course is presented, first...

... optimization oil well development machine learning efficiency information production control drillstem testing calculation production monitoring fetkovich correlation equation pet inflow performance relationship trans nodal analysis prosper tech software assisted approach Introduction...

... logic machine learning permeability ga sandstone journal rock type artificial intelligence basin reservoir characterization sandstone information overview ga reservoir fracture operation tight ga extraction Introduction Global Tight Gas Resource China Tight Gas Reservoirs...

... and experimental costs. This study aimed to develop a machine-learning model correlating the pore size distribution, pore volume, surface area, temperature, and pressure of activated carbons to their CO 2 adsorption capacity. The Cochran model was used to determine the minimum number of data samples required...

... during a 20-year simulation period, the differential evolution optimization method outperformed the particle swarm optimization. unconventional play optimization problem evolutionary algorithm recovery heavy oil reservoir geologist complex reservoir reservoir petroleum play type machine...

.... However, predicting the extent of mud loss before drilling specific formations would greatly benefit engineers. This study aims to predict mud loss using advanced boosting machine learning frameworks, addressing the need for more accurate forecasting tools. We evaluated three ensemble boosting algorithms...

... sedimentary rock geology drilling fluid property drilling fluid formulation drilling fluids and materials machine learning journal gnt petroleum science yakasai drilling fluid selection and formulation sciencedirect graphene dispersion engineering concentration nanoparticle application...

.... This pioneering research develops ENERLIZER, the first open-source solution to integrate real-time data analytics and machine learning for end-to-end automated insights into robust multi-energy optimization. This solution is developed on the Streamlit framework and leverages integration with real-time databases...

...Literature Review Anomaly detection is a significant issue that has garnered attention across various research fields and practical domains. Numerous methods for anomaly detection have been custom-tailored for drilling purposes. Among these are Statistical, Machine Learning-Based, Clustering...

... of incentives to foreign investors, political stability, and enhanced legal frameworks. social responsibility artificial intelligence sustainability sustainable development ga export energy economics nigeria export machine learning investment fdi gdp non-oil export natural gas export...