... engineers’ expectation by appraising the pros and cons of each optimisation strategies. machine learning reservoir simulation accuracy evolutionary algorithm history matching proxy neural network objective function vector simulation run optimisation algorithm proxy model neuron initial...

... tornado sensitivity chart for the fluid efficiency model. Figure 10 Comparative results (top, proppant related; bottom, fluid efficiency). Abstract Abstract With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing...

... (PVT) laboratory experiments; nonetheless, these experiments' results are time-consuming and costly. Therefore, many studies used alternative methods, namely empirical correlations (using regression techniques) and machine learning to determine the formation volume factor. Unfortunately, the previous...

... better than the LR and LDA models that are based on linear mapping in the prediction of grain size classes from wireline logs. We conclude that the machine learning methodology represented by the RF model in this study is capable of handling the complexity and uncertainties in the grain size estimation...

... basin production control production logging velocity map machine learning artificial intelligence gas production flow array sensing tool perforation recirculation optical probe wellbore hold-up map reservoir surveillance fast plt probe standard plt electrical probe production...

... create their petroleum exploration and exploitation strategy in Indonesia if they take this information into consideration, which will benefit both the government and contractors. machine learning oil and gas reserves asset and portfolio management artificial intelligence eastern region boe...

... cycle efficiency drillstem testing efficiency machine learning asset and portfolio management digital solution suite big data drillstem/well testing information shripad biniwale petroleum engineer database high-frequency data objective integrated operation engineering model spe...

... in thermally challenging applications across the Middle East, North Africa region, and in West Texas. bit design thermal wear machine learning upstream oil & gas onepetro drill bit shoulder region cooling cutting structure bit selection thermal index design process pdc cutter artificial...

... & gas well 1 machine learning reservoir simulation dynamic property saturation perforation time step realization neural network fluid dynamics workflow flow simulator dataset scenario dnn model reservoir model flow model u-net model proxy model permeability realization 13 spe...

... economics reservoir geomechanics asset and portfolio management machine learning production monitoring production logging us government oil shale unconventional resource economics vaca muerta formation production forecasting directional drilling fracture characterization heterogeneity...

.... artificial intelligence accuracy neural network information machine learning upstream oil & gas spe annual technical conference vibration prediction result recurrent neural network operation lstm network lstm recurrent neural network drilling deep learning prediction vibration level...

... convoluted with changes in rock and drilling data. Recent approaches for bit wear estimation also include model-based and traditional supervised machine learning methods, which are usually costly and time-consuming. In this study, we developed a bi-directional long short-term memory-based variational...

... a composite risk probability map, where there are different risk levels at different locations. Combining with engineering constraints, the CO 2 injection well locations were selected by optimization, avoiding the area with high risk. machine learning upstream oil & gas risk management...

... saturation, fracturing fluid volume. The hybrid model has been applied to the productivity prediction of 185 MFHWs in tight oil reservoirs in China, the prediction error was less than 5%. The new model can be used to predict production for MFHWs quickly and economically. machine learning modeling...

..., and data mining can be useful for almost all large-scale field development optimizations. fracturing materials proppant data preparation data mining hydraulic fracturing upstream oil & gas reservoir characterization asset and portfolio management fracturing fluid machine learning...

... Artificial Intelligence (AI)/Machine Learning (ML) is a way to build an automating process with minimal human intervention, improving consistency and efficiency of well log prediction and interpretation. Nowadays, the argument is whether AI-ML should base on a statistically self-calibrating or knowledge...

... as well as minimizing cost, time, and material usage. machine learning artificial intelligence strength wellbore integrity nahr umr shale correlation brazilian test reservoir characterization wellbore design laboratory tests zubair sandstone iraq petroleum engineer shale rock sample...

.... To address this problem, flow-based two-phase upscaling techniques are combined with machine learning algorithms, in which the flow-based two-phase upscaling is needed only for a small fraction of coarse interfaces (or blocks), while the upscaled two-phase parameters for the rest of the coarse interfaces...

... asset. machine learning upstream oil & gas gvf 1 0 intake gvf multiphase pump rotational speed svip artificial lift system hydraulic jet pump performance curve test data artificial intelligence flow rate dimensionless pressure difference performance equation equation rpm...

... as main reference to assess the effectiveness of learning from incidents system in the company ( Jacobsson et al., 2011 ). machine learning organizational memory learning process investigation artificial intelligence safety culture analysis result procedure upstream oil & gas...