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Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models

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Abstract

In gas condensate reservoirs, the dew point pressure (PDew) plays a significant role in gas and liquid assessment, reservoir characterisation, surface facility design, and reservoir simulation. Although field and laboratory measurements of PDew give accurate results, both approaches are time-consuming and resource-intensive; hence, a fast and accurate determination of PDew is very important. Equation of states (EoS) and empirical correlations are other alternative methods that are used for PDew determination. However, these methods are unable to fully capture the non-linear and complex relationships between fluid composition and PDew. Machine Learning (ML) methods, as reliable tools, have emerged in different aspects of engineering. In this study, for the first time, the application of different decision tree-based methods for the prediction of PDew is investigated. A comprehensive database, containing 681 samples (almost all the available experimental data set of pure and impure samples published from 1942 to 2018), is collected from open literature and different decision tree-based methods namely Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Extremely Randomised Tree (ET) are used for modelling. The statistical analysis of developed models' performance showed that the ET method yields the best predictions by Root Mean Squared Error (RMSE) and R2 values of 441 psi and 0.9227, respectively for the testing dataset. Moreover, the results show that the novel ET model has a better performance compared with existing models in the literature and EoSs for the prediction of PDew of gas condensate reservoirs.

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Data availability

The datasets analysed during the current study are available from the corresponding author upon request.

Abbreviations

A:

Arithmetic

AD:

Applicability domain

ACE:

Alternating conditional expectations

AI:

Artificial intelligence

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

BR:

Bayesian regularization

CCE:

Constant composition expansion

CMIS:

Committee machine intelligent system

COA:

Cuckoo optimization algorithm

CSA:

Coupled simulated annealing

CV:

Cross-validation

CVD:

Constant volume depletion

DT:

Decision tree

EGPR:

Evolutionary gaussian processes regression

EoS:

Equation of state

ET:

Extremely randomized tree

FL:

Fuzzy logic

G:

Geometric

GA:

Genetic algorithm

GB:

Gradient boosting

GEP:

Gene expression programming

GRG:

Generalized reduced gradient

H:

Hat Matrix

LM:

Levenberg–Marquardt

LSSVM:

Least-squares support-vector machines

MGGP:

Multi-gene genetic programming

MKF:

Mixed kernels function

ML:

Machine learning

MLP:

Multi-layer perceptron

NN:

Neural network

OOB:

Out of bag

PR:

Peng robinson

PSO:

Particle swarm optimization

PVT:

Pressure–Volume–Temperature

RBF:

Radial basis function

RF:

Random forest

RK:

Redlich–Kwong

SCEUA:

Shuffled complex evolution

SCG:

Scaled conjugate gradient

SRK:

Soave–Redlich–Kwong

SSA:

Salp swarm algorithm

SVM:

Support-vector machines

SW:

Schmidt–Wenzel

ZJ:

Zudkevitch–Joffe

C 1 :

Methane concentration (Fraction)

C 2 :

Ethane concentration (Fraction)

C 3 :

Propane concentration (Fraction)

C 4 :

Butane concentration (Fraction)

C 5 :

Pentane concentration (Fraction)

C 6 :

Hexane concentration (Fraction)

C 7 + :

Heptane plus concentration (Fraction)

CO 2 :

Carbon dioxide concentration (Fraction)

h :

Hat values

h * :

Warning leverage

H 2 S :

Hydrogen sulphide concentration (Fraction)

MAE :

Mean absolute error (%)

MAPE :

Mean absolute percentage error (%)

Max :

The maximum value

max_depth :

Maximum depth of a DT

max_features :

Maximum features to be used in the development of a DT model

Mean :

The mean value

Min :

The minimum value

MPE :

Mean percentage error (%)

MSE :

Mean squared error

MW C7 + :

Molecular weight of heptane plus fraction (g/mol)

n_estimator :

Number of estimators (trees) used in ensembled models

N 2 :

Nitrogen gas concentration (Fraction)

P :

Pressure (psi)

P Dew :

Dew point pressure (psi)

Res :

Residual (psi)

R 2 :

Coefficient of determination

RMSE :

Root mean squared error (psi)

SD :

Standard deviation (psi)

SR :

Standard residual

SG C7 + :

The specific gravity of heptane plus fraction

SMAPE :

Symmetric mean absolute percentage error (%)

t :

Target value (psi)

T :

Temperature (°F)

WMAPE :

Weighted mean absolute percentage error (%)

y :

Predicted value (psi)

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Authors and Affiliations

  1. School of Chemical Engineering, Discipline of Mining and Petroleum Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia

    Zohre Esmaeili-Jaghdan, Afshin Tatar, Amin Shokrollahi & Abbas Zeinijahromi

  2. Petrolab, Adelaide, SA, 5072, Australia

    Jan Bon

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Correspondence to Amin Shokrollahi.

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The original online version of this article was revised to include the supplementary files.

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Appendix: Instruction on how to use the developed models

Appendix: Instruction on how to use the developed models

The developed models are provided for the users. The models work using Python. The input data are received from an excel file. After the models predicted the outputs, they will be printed in the same excel file, in the sheet "Output". The model files and the code are also provided as supplementary files.

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Esmaeili-Jaghdan, Z., Tatar, A., Shokrollahi, A. et al. Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models. Neural Comput & Applic 36, 1973–1995 (2024). https://doi.org/10.1007/s00521-023-09201-9

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