... are fundamental for several aspects in CCUS projects, including reservoir modeling, geomechanics, geochemistry, monitoring phases and risk management. machine learning reservoir characterization data mining subsurface storage well logging artificial intelligence climate change log analysis ccus...

... in sections without core; as input we use conventional (gamma ray, density, neutron, sonic), NMR, BHI (acoustic imaging) logs, core CT images and physical porosity and permeability measurements from plugs. This new workflow combines supervised deep learning and unsupervised machine learning methods...

... of data analysis using artificial intelligence and machine learning techniques on a single well or wells at field level in time-lapse provides a consistent, efficient, and reliable answer for deciding whether to operate the well at a lower operating envelope or to plan an intervention/workover...

... the sensors, select the better ones in agreement with expert evaluation. reservoir characterization health & medicine artificial intelligence deep learning regression upstream oil & gas validation set network score machine learning neural network computer vision correspond...

... drilling fluid management & disposal real time system well logging equation of state pvt measurement log analysis drilling fluids and materials pvt sample threshold mud gas spwla-2022-0007 composition castberg field accuracy prediction spwla 63 machine learning artificial...

... accurate GOR predictive model in 2019 from advanced surface data based on a machine learning algorithm. Since then, the method has been applied to both conventional and unconventional fields with good results. For our Norwegian operational units, we are developing a real-time service for fluid...

... require the usage of Digital Signal Processing (DSP) to reduce the amount of stored data and data-driven techniques such as machine learning (ML) for analysis. Distributed sensing data is sampled at high rates; up to 10,000 samples per second and depth for Distributed Acoustic Sensing (DAS), and one...

... upstream oil & gas annual logging symposium pixel machine learning accuracy spwla 63 artificial intelligence classifier workflow lithofacies predictor feature ct-scan image rock classification algorithm core photo permeability spwla-2022-0018 vertical resolution rd annual...

... the Maxwell-Wagner effect. We aim to utilize this connection between pore geometry and dielectric dispersion to predict permeability using a core-data trained supervised machine learning model on dielectric dispersion wireline logging arrays. It builds upon a previous single-well study (Norbisrath, 2018...

... of the reservoir structure. machine learning real time system log analysis well logging lwd neural network geosteering upstream oil & gas drilling data acquisition logging while drilling drilling operation drilling measurement artificial intelligence reservoir navigation detection...

... drilling fluid chemistry drilling fluid selection and formulation machine learning well logging formation damage drilling fluids and materials fluid loss control simulation porosity water saturation spwla 63 drilling fluid property upstream oil & gas borehole log analysis...

... of permeability by following a machine learning based approach. Firstly, a vast range of MICP test results (246 samples) related to tight sandstones is gathered with a permeability range of 0.001 to 70 millidarcy. After quality checking of dataset, different theoretical permeability models are tested...

... massive sand spwla-2022-0035 reservoir characterization upstream oil & gas water saturation conductivity log analysis macroporosity porosity volume fraction shaly sand rd annual logging symposium pore-filling clay resistivity machine learning artificial intelligence dispersed clay...

... to be fixed empirically. We developed a deconvolution method using Machine Learning technique to invert the resistivity log accurately and fast while the bed boundaries are automatically inverted accurately within 0.5-ft that is the sampling interval of the log data. We used Machine Learning regressors...

... of applying KINMF is for T2 relaxation times less than 100 ms and it also significantly improved computational times (enhanced real-time data processing). This should lead to broader applicability and improved physical interpretation of NMR data. machine learning upstream oil & gas spwla 63...

... chemistry drilling fluid selection and formulation machine learning well logging log analysis drilling fluid property drilling fluids and materials upstream oil & gas fluid loss control lxo drilling fluid formulation reservoir characterization artificial intelligence structural geology...

... promising machine-learning (ML) methods for predicting missing logs. We include the following methods in the comparison: the window-based convolutional neural network autoencoder (WAE), the pointwise fully connected autoencoder (PAE), and the tree-based pointwise eXtreme Gradient Boosting (XGBoost). We also...

... data quality machine learning data mining badlog flag outlier well logging annual logging symposium threshold log analysis upstream oil & gas detection artificial intelligence spwla-2022-0068 badlog logplot example isolation forest voting system badlog algorithm den rd...

... well logging reservoir characterization machine learning artificial intelligence log analysis spwla-2022-0069 prediction workflow formation property mineralogy interpretation spectroscopy tool concentration good match schlumberger appraisal well rd annual logging symposium...

... Abstract There have been numerous efforts exploring the application of machine learning (ML) techniques for field-scale automated interpretation for well log data. A critical prerequisite for automatic interpretation via computational means is to ensure that the log characteristics...