... will accelerate EKC to reach its turning point. machine learning artificial intelligence knowledge management social responsibility sustainability us government platform galloprovincialis ecosystem service upstream oil & gas artificial reef sustainable development human computer interaction...

... availability of real-time field data, digitalized information and documentation, has made technically and economically feasible to approach operating issues by applying new tools and methodologies. For example, Big Data and Machine Learning techniques, implemented with success in other businesses, have been...

... Abstract This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to tackle and manage both major upsets...

... Drilling: An Industry First Technology Advancement for Borehole Image Acquisition, OTC-30169-MS Zhang, T., Yang, S., Shrivastava, C., A, A., Bize-Forest, N., 2021, A new pseudo-velocity based method to mitigate LWD image interpretation risks, SPWLA 62th Annual Symposium, May 17-20 11 machine learning...

... and ultimately productions forecasts; therefore, both the quantification of the structural uncertainty and its calibration through History matching process are crucial (Seiler, Rivenaes, Aenonsen, & Evensen, 2009). reservoir simulation parameterization localization lo forte stefania machine...

... and ontologies up to machine learning methods. The edges of the Knowledge Graphs are created by using a number of Relationship Aggregators, i.e. software that can automatically link to each other the concepts identified by the Annotators. Annotators and Aggregators can be graphically configured by the users...

... of installed sensors, it is often required to reduce the number of variables before building machine learning models. With the increased popularity of neural networks, traditional methods used for the reduction of the dimensionality of variables, such as Principal Component Analysis, have been replaced...

... Abstract Today and more than ever before, there is a necessity to reduce drilling time and optimize operations. This paper discusses the implementation of a system that monitors rig and equipment parameters and applies machine learning algorithms to reduce NPT, improve equipment efficiency...

... to be representative of the real geomechanical background. In this picture, with a very limited acquired log dataset, Machine Learning (ML) techniques allow to address the issue by means of the elastic log triplet reconstruction. This approach consists in choosing the most representative wells with complete datasets...

... with a geomechanical significance. Generation of seismic attributes describing the rock elastic properties (e.g. compressional and shear moduli, bulk density) that are relevant to lithology and fluid discrimination as well as to geomechanics, providing a full description of the overall series. (2) Machine Learning...

...PREDICTING RESERVOIR FLUID PROPERTIES FROM ADVANCED MUD GAS ANALYSIS USING MACHINE LEARNING MODELS Shahnawaz Molla, Maneesh Pisharat, Ilaria De Santo, Ivan Fornasier, Schlumberger This paper was presented at the 15th OMC Med Energy Conference and Exhibition in Ravenna, Italy, September 28-30, 2021...

...) and, finally, porosity. It is therefore important to recognize groups of similar regions inside image logs, in terms of textural patterns, as representing geological and petrophysical events of facies. machine learning log analysis structural geology production control reservoir surveillance...

... displacements. artificial intelligence displacement map upstream oil & gas health & medicine gnss station corner reflector displacement rate sar measurement geometry machine learning resolution support oil production activity availability time series monitoring dataset gnss...

... artificial intelligence criteria facies machine learning accuracy classifier comparative analysis conventional method programme committee prediction upstream oil & gas libyan carbonate reservoir machine learning classifier mabruk oil operation prediction accuracy facies...

... permeability curves and the rock wettability based on a large amount of water-oil relative permeability experimental results from offshore fields in Gulf of Suez basin. A new machine learning neural network developed which represents an alternative approach to study relative permeability and rock wettability...

.... In this pilot site the service is developed and deployed. A system calibration phase allowed the refinement of the rainfall thresholds, thanks to machine learning system , which contributes to the increase of the system reliability. The work has been structured in three tasks: 1. Analysis of the information...

.... It results in almost 900 t/y CO 2 fixation and about 500 t/y oxygen production. The biomass quality is very high, and the algal oil is suitable for feeding Eni Refineries. water management machine learning sustainable development biofuel health & medicine upstream oil & gas carbon...

.... These data are then analysed through Machine Learning and Deep Learning algorithms which are incorporated within the agents. The machine learning algorithms estimate the current state of the equipment and provide a set of KPIs in order to understand both the production efficiency and the health status...

... months to complete. For this reason, we developed an innovative Machine Learning-based support tool to facilitate and speed-up the whole process of biomarkers examination and interpretation. The core of tool is an advanced clustering method that allows expressing biomarkers data as a combination...

... & gas horizontal cell grid resolution hawie geologic modeling evolution machine learning ifp energy nouvelle artificial intelligence east-mediterranean region thermal burial nader uncertainty modeling sediment petroleum system miocene interval 1 INTEGRATED STRATIGRAPHIC, THERMAL/BURIAL...