... by approximately 465,000 BBLS (observed on 9 wells between Oct-2019 to Dec-2021). upstream oil & gas data quality prototype enhanced recovery conformance improvement machine learning prediction petroleum engineer estimation workflow requirement operational efficiency artificial intelligence...

... natural language information extraction optical character recognition machine learning co 2 workflow application nlp automating carbon storage site assessment keyword production inc shell global solution us inc Introduction The IPCC (Intergovernmental Panel on Climate Change) and IEA...

... SuccessFactors for all its core processes makes integrating LMS with other HR systems and modules much more practical. artificial intelligence upstream oil & gas personnel competence educational setting learner reality human computer interaction machine learning benefit pdo development...

... climate change gravity injection gravity gradient neural network sustainable development taranaki basin monitoring co2 migration sustainability social responsibility machine learning new zealand subsurface storage quantum gravity ai framework application reservoir characterization...

... the operational parameters of an FPSO with carbon capture, use, and storage (CCUS) contribute to the overall effect. The input parameters for the sensitivity analysis are chosen from some thermodynamic and structural design variables. To accomplish the objectives, four machine learning-based screening analysis...

... permeability curve and production surveillance data to predict the production performance. The commonly used methods include water drive characteristic curve, logistic prediction model ( Yu Qitai and Jin Hongwei, 1995 ; Yu Qitai, 1998 , 1999 ), etc. In recent years, with the development of machine learning...

... Round-robin tests and laboratory audits have demonstrated that reservoir fluid compositions measurements can be systematically uncertain. To reduce compositional uncertainties, this work uses Machine Learning (ML) algorithms to update the existing measured fluid compositions and applies a compositional...

... Abstract With increased popularity and success of Machine-Learning (ML) Techniques, there is continued interest in developing new tools while exploring the boundaries of the technique. Here we use ML techniques to identify reservoir engineering, geological and development features...

... scenarios proved to be key to reach all of these objectives. fluid dynamics ensemble risk management machine learning realization permeability upstream oil & gas flow in porous media artificial intelligence workflow facies reservoir simulation modeling & simulation reservoir...

..., this method relies solely on production data, ignoring the geology and various production mechanisms. Process automation and machine learning workflows can then be of great help in this second case but remain generally limited, lacking transverse features for the geology and production mechanisms...

... Given the challenges of the field discussed in this paper, there was a strong need of using an approach which could solve at least some of the key problems if not all in an efficient and technically correct manner. The project team adapted a novel Machine Learning and Data Analytics approach...

... interpretation and analysis workflow for formation testing data. We aim to leverage human-in-the-loop automation to an entire workflow using machine-learning (ML) algorithms, reducing turnaround time from weeks to minutes while also generating new insights. Using a combination of traditional and ML algorithms...

... & gas asset and portfolio management optimization information fusion machine learning realization reservoir characterization ensemble infill drilling conference multidisciplinary data integration uncertainty paper evaluation workflow simulation artificial intelligence well placement...

... of the injected fluid. With such well data and viscosity measurements, calculating the viscosity becomes a machine learning regression problem. Training the machine learning regression methods on the actual inline and laboratory-measured polymer viscosity has demonstrated that VVM is a promising, high-accuracy...

... dynamics optimization problem scaling method reservoir characterization grid artificial intelligence machine learning nuc grid algorithm evolution iteration sequence optimization algorithm reservoir simulation enhanced recovery optimization workflow transmissibility flow diagnostic...

... Simonov et al. (2019, SPE-196639) developed a similar approach to optimize miscible gas injection with regards to cumulative oil production using a surrogate model constructed from other Machine Learning algorithms such as Random Forest. The initial set of almost 1000 numerical simulations...

.... Statistical and machine learning techniques are combined to assess neighborhood performance and geologic risks, along with forecasting the future production performance of the proposed targets. Finally, a comprehensive vetting and sorting framework is presented to ensure the final set of identified...

... long production history with stable reservoir performance. upstream oil & gas production monitoring artificial intelligence forecasting production control saturation history forecast reservoir surveillance machine learning algorithm dca k-means clustering ai assisted well...

... miscibility pressure normalized mole fraction journal model co 2 prediction displacement composition petroleum science upstream oil & gas enhanced recovery machine learning mmp determination dindoruk neuron engineering Introduction Currently majority of the oil production is coming...

... 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...