.... neural network machine learning drilling operation artificial intelligence deep learning in-seam drilling engineering drill bit gamma ray correlation rop complex reservoir upstream oil & gas prediction rate of penetration formation type noncoal coal noncoal classification regression...

... Abstract Two main limitations occur when selecting a model with the best predictive accuracy over a set of candidates to extrapolate future production of wells. First, from a Bayesian standpoint, all models are interpretations of the data and thus, they are always incomplete. Second, we could...

... generate phase envelopes using the overall compositions predicted using the PINN and DL models for several fluid mixtures in the test data. These results show the importance of incorporating the thermodynamic constraints into DL models. Keywords: Physics-informed neural network, compositional modeling...

... and/or time-consuming to achieve in laboratory studies and with mechanistic foam models. Empirical correlations are unable to make rapid and accurate predictions when different combinations of all these parameters are considered. Therefore, this paper explores the use of simple-to-use, relatively fast...

... Abstract As a natural gas well ages, liquid loading is frequently encountered, accompanied by the tubing scaling and corrosion, leading to the decrease of gas production rate and many other side effects, which may in turn cease the gas production. Thus, to accurately predict liquid loading...

... into deep learning is proposed to forecast production in tight reservoirs. In the purely data driven approach, model training is driven solely by a data-based cost function which reflects the differences between model predictions and observations. In the proposed approach, a physics-based cost function...

...% in the gas mixtures significantly reduces the MMP of the oil-gas system (from 4366 psi for oil-C 1 to 1467 psi for oil-C 1 /C 2 with 71.3 mol% C 2 ), and increases the oil swelling factor. The MMP values predicted by plotting two-phase equilibrium data on ternary diagrams appear to be in good agreement...

... multiphase flow linear regression model flow rate linear regression water flow rate interaction FBHP pairwise interaction predictor variable CSG well coal seam gas Upstream Oil & Gas neural network model prediction prediction accuracy neural network approach Akaike , H. 1974...

...) is limited. To enhance the performance on coal prediction, two data manipulation techniques [naive random oversampling technique (NROS) and synthetic minority oversampling technique (SMOTE)] are separately coupled with machine learning algorithms. Case studies are performed with data from six wells...

... propagation of pressure in groundwater and reservoir models. Upstream Oil & Gas well logging Reservoir Characterization Queensland log analysis simple model walloon coal measure inversion process realisation prediction inversion geology hydraulic conductivity boundary sequence...