... to understanding the provenance of the Brent Group in a region of the North Sea. We show how the study design enables methods of advanced analytics, where the extended measurement set can be used to train predictive models. In our data analysis we utilize boosting threes to predict e.g., XRD mineralogy from XRF...

... these classical approaches require manual input, leading to a degree of subjectivity in formation evaluation. ABSTRACT General machine learning-based algorithms have been developed to predict important matrix properties and an estimate of their uncertainties. Grain density, neutron porosities, cross sections...

..., anisotropic model and an alteration model for near wellbore concentration effects. The training datasets are labeled with these physical models. Excellent accuracy was achieved with neural network training. We then applied the classifier to field prediction, where scattered dispersion points are first...

...SPWLA 63rd Annual Symposium, Stavanger, Norway June 11-15, 2022 DOI: 10.30632/SPWLA-2022-0112 SEQUENTIAL MULTI-REALIZATION PROBABILISTIC INTERPRETATION OF WELL LOGS AND GEOLOGICAL PREDICTION BY A DEEP-LEARNING METHOD Sergey Alyaev1, Adrian Ambrus1, Nazanin Jahani1, Ahmed H. Elsheikh2 1 NORCE...

... 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...

... Abstract Advanced mud gas logging has been used in the oil industry for about 25 years. However, it has been challenging to predict reservoir fluid properties quantitatively (e.g., gas oil ratio – GOR) from only the advanced mud gas data (AMG) while drilling. Yang et al. proposed the first...

... for determining different petrophysical properties including porosity, and pore throat distribution. The matrix permeability is dependent on the pore size distribution but is not directly measured from MICP tests. In this work, we consider distinct parameters derived from MICP tests for the prediction...

... Abstract To automate log interpretation at field scale, computational methods used to predict partially or entirely missing logs can be valuable. Such approaches could be potentially useful for correcting intervals of low-quality data, as well as predicting intervals outside the reservoir...

... 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...

... china university count ratio rd annual logging symposium upstream oil & gas prediction oil saturation petroleum lithology Abstract Dynamic monitoring of reservoir can reflect the physical response of fluid underground and clarify the oil and water distribution of the production, which...

... selected petrophysical parameters, they are highly correlated with water-filled porosity. Furthermore, if new conductivity and permittivity logs are generated with different petrophysical parameters, the correlations defined before can be used to predict water-filled porosity in the new datasets. We also...

... techniques in the subsurface domain, it is essential that the quality of the input data is carefully considered when working with these tools. If the input data is of poor quality, the impact on precision and accuracy of the prediction can be significant. Consequently, this can impact key decisions about...

... neural network algorithm that has been widely used in sequential data-based prediction to estimate geomechanical parameters. The prediction from log data can be conducted from two different aspects. 1) Single-Well prediction, the log data from a single well is divided into training data and testing data...

... and repaired online from May 17-20, 2021. whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm ABSTRACT uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically Subsurface analysis-driven field...

... of different sedimentary structures (i.e., classes) associated with different well deviations, with addition of gaps. The model was able to predict the proper class and highlight the transitions accurately. machine learning log analysis reservoir characterization well logging deep learning upstream...

... of Petrophysicists and Well Log INTRODUCTION Analysts (SPWLA) and the submitting authors. This paper was prepared for the SPWLA 62nd Annual Logging Symposium held The application of a Saturation Height Model (SHM) has online from May 17-20, 2021. as main purpose the water saturation prediction in the 3D geological...

... process. This paper focuses on three distinct petrophysical data scenarios where inputs are ranked prior to modelling: prediction of continuous permeability from discrete core measurements, porosity from multiple logging measurements and finally the prediction of key geomechanical properties. Each input...

... intelligence dakota group limitation symposium resistivity june 24 prediction july 29 requirement resistivity log ABSTRACT ABSTRACT This work compares traditional petrophysical workflows to machine learning methods to compute porosity and permeability in the Inyan Kara Formation of the Dakota...

... function danger permeability prediction equation drilling operation water saturation shear velocity flow in porous media upstream oil & gas prediction july 29 symposium electrical log computer june 24 ABSTRACT ABSTRACT Artificial Intelligence, or AI, is a method of data analysis...

...ABSTRACT ABSTRACT The computation of permeability is vital for reservoir characterization because it is a key parameter in the reservoir models used for estimating and optimizing hydrocarbon production. Permeability is routinely predicted as a correlation from near-wellbore formation properties...