... of equipment maintenance was reduced and thus maintenance cost and service cost were reduced as well. Moving towards the deployment of AI technology and digitalization world, a new requirement to deploy predictive maintenance analytics and tools elevated. The Centralized Predictive Analytics & Diagnostic...

... hydrocarbon recovery carbon footprint hybrid configuration flexibility workshop subsurface storage reduction criteria prediction scale offshore carbon capture Introduction CCS is a key enabler for carbon emission efforts for a more sustainable future while fulfilling energy demand. One...

... analysis and misses opportunities for well adjustment. Therefore, predicting the WCT of oil wells based on the limited available data is an essential work of oil field production analysis. In the early stage, the prediction methods of WCT and other production data are mainly based on the relative...

... for a cautious but robust adaptation and acceptance of ML techniques. machine learning artificial intelligence recovery factor permeability reserves evaluation reservoir complexity index relation development accuracy porosity predict recovery factor prediction rci upstream oil & gas...

... driven approach decision tree learning modeling reservoir simulation upstream oil & gas artificial intelligence communication history matching machine learning prediction reservoir modeling data science workflow regression limitation data analytic approach platform proxy...

... for their predictivity on unseen data. These ML-based proxy models were finally used to rapidly identify other optimal scenarios for each application based on several economic indicators. For the first application, numerical model calibration was obtained using one real coreflood experiment: measured pressure signal...

... learning trajectory doi interpolation algorithm paper castineira exhibition petroleum engineer optimization problem workflow constraint optimization algorithm directional drilling reservoir characterization society salehi well placement optimization leveraging geological prediction...

... injection pressures, CO 2 can be miscible easily but the initial operating cost would be high and there are safety concerns as well. These problems make accurate MMP prediction for the CO 2 flooding process important as it will help E & P companies to make increasing use of the CO 2 -EOR process...

... be sorted through generation of CO 2 foams in the reservoir. This work proposes the utilization of machine learning techniques, to predict foam flood performance which will thereby aid in optimization of laboratory core-flood experiments. This work is based upon consumption of large set of existing...

... with a predictive analytical model for choke performance and intelligent alarming, can significantly help asset in production planning and cost optimization while accurately regulating the field rates. First, the bulk of well-test data from corporate databases is integrated into an advanced digital platform...

... Abstract Calcium carbonate is a pH dependent inorganic mineral scale that is influenced by CO 2 and H 2 S partitioning. CaCO 3 prediction must therefore include accurate modelling of the aqueous phase and all hydrocarbon phases present. pH dependent scale prediction challenges...

... to establish the data foundation for data driven model training and forecasting. Then the Bi-GRU algorithm was utilized to forecast the performance of single well, which achieved high accuracy predictions with R2 of 0.91, RMSE of 198.93, and MAE of 85.22. After that, single-well temporal three-phase saturation...

... to NMR tools existing as relatively new technology, and the extra expense in logging runs and rig time, most wells lack these data. Most of the existing approaches to predict the rock porosity was developed on the Neutron-density porosity logs that usually are resulted in inaccurate estimation...

... their development. upstream oil & gas reservoir surveillance maintenance prediction flow metering production monitoring magnitude condition instability development production logging dp transmitter information north sea flow measurement workshop malfunction variation orifice production...

... illustration workflow artificial intelligence prediction module neural network smartvessel incident layout violation operation protective equipment hse use-case classifier comprehensive ai-based safety monitoring solution accuracy Introduction 100%-HSE culture is one of the main drivers...

... conditions during synthesis all can be estimated by the use of deep learning models. This paper presents yield prediction which is of high economic significance for chemical enhanced oil recovery, because they enable calculation of investment versus return. These models give us the conversion of reaction...

... predictive model learns from historical data how to define the proper coding of drilling operations based on operation description to minimize human interaction with the coding system for drilling operations. The approach utilizes a set of prediction models to predict the proper code combinations...

... artificial intelligence estimation integration inversion library prediction uhr seismic Introduction With the target of net-zero carbon emissions by 2050, the demand for renewable energy is increasing exponentially. The traditional oil and gas industry is transitioning through a new energy era...

... operation conventional artificial lift system afkar binary classifier classifier abnormal dynocard upstream oil & gas artificial lift system confusion matrix efficiency deferment abnormal development blind test working group prediction Introduction The pathfinder initiative...

... breakdown pressure is required. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time consuming process. Therefore, in this study, different machine learning models were efficiently utilized to predict the breakdown pressure of the tight rocks. In the first part...