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Artificial intelligence techniques and their application in oil and gas industry

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Abstract

Data are being continuously generated from various operational steps in the oil and gas industry. The recordings of these data and their proper utilization have become a major concern for the oil and gas industry. Decision making based on predictive as well as inferential data analytics helps in making accurate decisions within a short period of time. In spite of many challenges, the use of data analytics for decision making is increasing on a large-scale in the oil and gas industry. An appreciable amount of development has been done in the above area of research. Many complex problems may now be easily solved using Artificial Intelligence (AI) and Machine Learning (ML) techniques. Historical, as well as real-time data, can be assimilated to achieve higher production by gathering data from the gas/oil wells. Various analytical modeling techniques are now widely being used by the oil and gas sector to make a decision based on data analytics. This paper reviews the recent developments via applications of AI and ML techniques for efficient exploitation of the data obtained, starting from the exploration for crude oil to the distribution of its end products. A brief account of the acceptance and future of these techniques in the oil and gas industry is also discussed. Present work may provide a technical framework for choosing relevant technologies for effectively gaining the information from the large volume of data generated by the oil and gas industry.

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Data available on request from the authors.

Abbreviations

ARE:

Absolute Relative Error

ANN:

Artificial Neural Network

BDMT:

Big Data Methods and Tools

BHP:

Bottom Hole Pressure

BNN:

Bayesian Belief Network

DT:

Decision Tree

E&P:

Exploration and Production

EOR:

Enhanced Oil Recovery

ESP:

Electrical Submersible Pump

FL:

Fuzzy Logic

GA:

Genetic Algorithm

GBM:

Gradient Boosting Machine

LR:

Linear Regression

NDT:

Neural Decision Tree

MPD:

Measured Pressure Drilling

MSE:

Mean Squared Error

PCA:

Principal Component Analysis

PNN:

Probabilistic Neural Network

PVT:

Pressure Volume Temperature

RF:

Random Forest

SVM:

Support Vector Machine

SVR:

Support Vector Regression

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  1. Information Technology and Systems, Indian Institute of Management, Kashipur, 244713, India

    Sachin Choubey

  2. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    G. P. Karmakar

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  1. Sachin Choubey
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  2. G. P. Karmakar
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Correspondence to Sachin Choubey.

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Choubey, S., Karmakar, G.P. Artificial intelligence techniques and their application in oil and gas industry. Artif Intell Rev 54, 3665–3683 (2021). https://doi.org/10.1007/s10462-020-09935-1

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