Abstract
The authors investigate how artificial intelligence modifies a huge piece of the energy area, the oil and gas industry. This paper attempts to evaluate technical and non-technical factors affecting the adoption of machine learning technologies. The study includes machine learning development platforms, network architecture, and opportunities and challenges of adopting machine learning technologies in the oil and gas industry. The authors elaborate on the three different sectors in this industry namely upstream, midstream, and downstream. Herein, a review is presented to evaluate the applications and scope of machine learning in the oil and gas industry to optimize the upstream operations (including exploration, drilling, reservoir, and production), midstream operations (including transportation using pipelines, ships, and road vehicles), and downstream operations (including production of refinery products like fuels, lubricants, and plastics). Enhanced processing of seismic data is illustrated which provides the industry with a better understanding of machine learning applications. Basing on the investigation of AI implementation prospects and the survey of subsisting implementations, they diagram the latest patterns in creating AI-based instruments and distinguish their impacts on speeding up and de-gambling measures in the business. They examine AI proposition and calculations, just as the job and accessibility of information in the portion. Furthermore, they examine the principal non-specialized difficulties that forestall the concentrated use of man-made brainpower in the oil and gas industry (OGI), identified with information, individuals, and new types of joint effort. They additionally diagram potential situations of how man-made reasoning will create in the OGI and how it might transform it later on (in 5, 10, and 20 years).
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Abbreviations
- AI:
-
artificial intelligence
- OGI:
-
oil and gas industry
- ML:
-
machine learning
- IoT:
-
Internet of things
- O&G:
-
oil and gas
- HPC:
-
high-performance computing
- ANN:
-
artificial neural network
- MANN:
-
modular artificial network
- LNG:
-
liquefied natural gas
- MAS:
-
multi-agent system
- SGCP:
-
simple gateway control protocol
- GOSP:
-
gas–oil separation plant
- PSO:
-
particle swarm optimization
- PRISMS:
-
petroleum refinery incorporated supply chain modeler and simulator
- SVM:
-
support vector machine
- SCADA:
-
supervisory control and data acquisition
- DOT:
-
designated order turn around
- EFM:
-
enterprise feedback management
- LIDAR:
-
light detection and ranging
- DEA:
-
data envelopment analysis
- PCA:
-
principal component analysis
- RUL:
-
remaining useful life
- BP:
-
British petroleum
- GRNN:
-
general regression neural network
- BPNN:
-
backpropagation neural network
- DRIFT:
-
diffuse reflectance infrared Fourier transform
- ROP:
-
rate of penetration
- MILP:
-
mixed-integer linear programming
- DNN:
-
deep neural network
- RF:
-
random forest
- KPI:
-
key performance indicator
- LP:
-
linear programming
- NM-MAS:
-
network management multi-agent system
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The authors are grateful to the Department of Chemical Engineering, Manipal Institute of Technology, and Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University, for the permission to publish this research.
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Gupta, ., Shah, M. A comprehensive study on artificial intelligence in oil and gas sector. Environ Sci Pollut Res 29, 50984–50997 (2022). https://doi.org/10.1007/s11356-021-15379-z
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DOI: https://doi.org/10.1007/s11356-021-15379-z