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Application of artificial intelligence techniques in the petroleum industry: a review

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Abstract

In recent years, artificial intelligence (AI) has been widely applied to optimization problems in the petroleum exploration and production industry. This survey offers a detailed literature review based on different types of AI algorithms, their application areas in the petroleum industry, publication year, and geographical regions of their development. For this purpose, we classify AI methods into four main categories including evolutionary algorithms, swarm intelligence, fuzzy logic, and artificial neural networks. Additionally, we examine these types of algorithms with respect to their applications in petroleum engineering. The review highlights the exceptional performance of AI methods in optimization of various objective functions essential for industrial decision making including minimum miscibility pressure, oil production rate, and volume of \(\hbox {CO}_{2}\) sequestration. Furthermore, hybridization and/or combination of various AI techniques can be successfully applied to solve important optimization problems and obtain better solutions. The detailed descriptions provided in this review serve as a comprehensive reference of AI optimization techniques for further studies and research in this area.

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Abbreviations

ACO:

Ant colony optimization

AI:

Artificial intelligence

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural networks

ARS:

Adaptive random search

BP-ANN:

Back propagation artificial neural networks

CCDE:

Cooperative coevolutionary differential evolution

CMA-ES:

Covariance matrix adaptation evolution strategy

CR:

Crossover probability rate

CSOR:

Cumulative steam to oil ratio

DE:

Differential evolution

E¶:

Exploration and production

EA:

Evolutionary algorithms

F:

The scaling factor

FL:

Fuzzy logic

FIS:

Fuzzy inference system

GA:

Genetic algorithms

gbest :

The other’s best experiences

HDE:

Hybrid differential evolution

HF:

Hydraulic fracturing

ICA:

Imperialist competitive algorithm

MMP:

Minimum miscibility pressure

NA:

Neighborhood algorithm

NAB:

Neighborhood approximation Bayes

NP:

The size of population

NPV:

Net present value

pbest :

A particle’s best experience

PSO:

Particle swarm optimization

SAGD:

Steam assisted gravity drainage

SI:

Swarm intelligence

SPSA:

Simultaneous perturbation stochastic approximation

UD:

Uniform design

VAPEX:

Vapor extraction

WAG:

Water alternative gas

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  1. Department of Chemical and Petroleum Engineering, Schulich of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada

    Hamid Rahmanifard & Tatyana Plaksina

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Rahmanifard, H., Plaksina, T. Application of artificial intelligence techniques in the petroleum industry: a review. Artif Intell Rev 52, 2295–2318 (2019). https://doi.org/10.1007/s10462-018-9612-8

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