... diagnosis intelligent monitoring email ieee trans ind electron example machine learning oil production asset correlation operator sensor References References J. Picabea , M. Maestri , M. Cassanello , G. Horowitz , Hybrid model for fault detection and diagnosis...

... was the selected project in Repsol drilling portfolio to test the use of the in-house artificial intelligence tool to improve historical performance by following a set of drilling parameter recommendations being analyzed with both historical and real time data by the embedded Machine Learning models. The tool...

... Upper Magdalena Valley basin. This enhancement is provided by combining Machine Learning techniques for the prediction of downhole pump failures by various agents. This project's scope is predictive failures with Analytics insight. The periodicity of failures and the impact of each of these, measured...

... upstream oil & gas mexico government structural geology rock type artificial intelligence complex reservoir united states government bitumen oil sand petroleum play type machine learning canada government heavy oil play porosity reservoir characterization geertsma compressibility...

... and machine learning would be remarkable towards pre-processing, organizing, and analyzing available data of mature fields, with in many cases, is insufficient to provide a good reservoir characterization; in addition, in some cases data is inconsistent, heterogeneous, and poor-quality, contributing...

..., 1989 ) ( Sharaf, et al., 2019 ) Table 2 Types of Machine Learning Models. Adapted from( Géron, 2019 ) Table 4 Performance Metrics used for the evaluation of ML Models Figure 22 Confusion Matrix for each model. Precision (p) and Recall (r...

... do not have this data. This work presents the Machine Learning as a methodology to estimate TOC, PHIT, cementation factor (M) and saturation exponent (N) with basic logs. Synthetic logs are generated with different predictive models, taking readily available conventional wire logs as input data...

..., an integrated machine learning (ML) model was proposed that allows to identify the risk of organic precipitation damage and estimate the asphaltene onset pressure (AOP). In addition, an estimation of the association parameters to estimate the AOP using a Cubic-Plus-Association (CPA) equation of state (EoS...

... drawbacks. In this paper, we develop a decision-making waterflooding framework, where an optimization component has embedded financial and machine learning models, to establish the wells operational plan obtaining the maximum profit and the best oilfield management. In this work, we use a reduced-order...

.... A combination of data-driven model reduction strategies and machine learning (deep-neural networks ANN) will be used to simultaneously predict state and the best correlated input-output matching. We remove the states that are hard to control and observe in the bilinear space by introducing a loss function...

... of S W . Abstract Artificial Intelligence (AI) and Machine Learning (ML) have made log reconstruction process much easier, faster, and economical by means of learning through uncounted experiences from already explored and developed reservoirs, their rock properties, and the cross-ponding...

... in real-time, allowing for continuous optimization of the system. This is achieved by integrating various technologies such as sensors, IoT devices, and machine learning algorithms, which provide data on the physical asset and its environment. The data is then processed and analyzed, providing insights...

... modeling & simulation reservoir simulation trajectory design simulation machine learning artificial intelligence drilling equipment drillstring dynamics simulation consortium exhibition simulator bit selection well planning directional drilling upstream oil & gas proc consortium...

..., it will allow to intrinsically take into account variables such as geomechanics properties in the model. drilling fluids and materials artificial intelligence natural language drilling operation node upstream oil & gas machine learning neural network accuracy computational model testing...

... of POD-DEIM method to reduce the computational cost for multi-phase, multi-component 3D reservoir model. modeling & simulation fluid dynamics artificial intelligence upstream oil & gas machine learning flow in porous media secondary variable enhanced recovery reservoir simulation...

... into the problem systematically. Our Bayesian approach also enables us to infer the uncertainty of the deconvolved solution directly from the posterior distribution. machine learning production control production monitoring drillstem testing artificial intelligence bayesian inference pressure transient...

... data mining modeling & simulation fluid modeling scaling method reservoir surveillance flow in porous media production control enhanced recovery asset and portfolio management machine learning production monitoring artificial intelligence hydraulic fracturing drillstem testing...

.... The parameters were selected from genetic algorithms using the technique of auto-machine learning. The model was developed with a database of 919 fields, from different public sources, and consists of three stages: 1) A dimensional reduction of the initial variables using t-SNE (t-Distributed Stochastic...

... solution id-eor project maintenance cost recommendation production network machine learning artificial intelligence steam-assisted gravity drainage upstream oil & gas information workflow opex reduction project management sagd reduction reservoir model automation injection field...

... upstream oil & gas enhanced recovery injector predictive model pareto front algorithm objective predictivity optimization objective machine learning waterflooding optimization redistribution argentina reservoir physics scenario waterflood project traditional reservoir physics basin...